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Operation and performance of the ATLAS tile calorimeter in LHC Run 2

December 2024
The European Physical Journal C 84(12)

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The ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report.

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Eur. Phys. J. C (2024) 84:1313

https://doi.org/10.1140/epjc/s10052-024-13151-4

Regular Article - Experimental Physics

Operation and performance of the ATLAS tile calorimeter in LHC

Run 2

ATLAS Collaboration

CERN, 1211 Geneva 23, Switzerland

Received: 30 January 2024 / Accepted: 22 July 2024

Abstract The ATLAS tile calorimeter (TileCal) is the

hadronic sampling calorimeter covering the central region

of the ATLAS detector at the Large Hadron Collider (LHC).

This paper gives an overview of the calorimeter’s operation

and performance during the years 2015–2018 (Run 2). In

this period, ATLAS collected proton–proton collision data

at a centre-of-mass energy of 13 TeV and the TileCal was

99.65% efﬁcient for data-taking. The signal reconstruction,

the calibration procedures, and the detector operational sta-

tus are presented. The performance of two ATLAS trigger

systems making use of TileCal information, the minimum-

bias trigger scintillators and the tile muon trigger, is dis-

cussed. Studies of radiation effects allow the degradation

of the output signals at the end of the LHC and HL-LHC

operations to be estimated. Finally, the TileCal response to

isolated muons, hadrons and jets from proton–proton colli-

sions is presented. The energy and time calibration methods

performed excellently, resulting in good stability and unifor-

mity of the calorimeter response during Run 2. The setting of

the energy scale was performed with an uncertainty of 2%.

The results demonstrate that the performance is in accordance

with speciﬁcations deﬁned in the Technical Design Report.

Contents

1 Introduction ......................

2 Experimental setup ...................

2.1 Tile calorimeter ..................

2.2 Readout electronics ................

2.3 Calibration systems ................

2.3.1 Charge injection system ..........

2.3.2 Integrator readout of minimum-bias collisions

2.3.3 Caesium system ...............

2.3.4 Laser system ................

3 Reconstruction and calibration of signals in physics

events ..........................

e-mail: atlas.publications@cern.ch

3.1 Signal amplitude .................

3.1.1 Channel timing calibration .........

3.1.2 Channel timing monitoring .........

3.2 The conversion factor from ADC counts to pC ..

3.3 Signal measurement in GeV ...........

3.4 Monitoring of the PMT, tile and ﬁbre response

with the caesium system .............

3.5 Monitoring of the PMT, tile and ﬁbre response

with minimum-bias collisions ...........

3.6 Monitoring of the PMT response with the laser

system .......................

3.7 The calibration procedure .............

3.8 Signal reconstruction of simulated events .....

4 Trigger signals .....................

4.1 Level-1 calorimeter trigger ............

4.2 Tile muon trigger .................

4.3 Minimum-bias trigger scintillators ........

5 Radiation exposure effects ...............

5.1 Comparison of laser, caesium and minimum-

bias measurements ................

5.2 Degradation of scintillator tiles and WLS ﬁbres .

5.3 Degradation of the MBTS system .........

6 Data quality analysis and operation ..........

6.1 Detector control system ..............

6.2 Calibration run validation .............

6.3 Online data quality assessment and monitoring ..

6.4 Ofﬂine data quality review ............

6.5 Summary of TileCal operations in Run 2 .....

7 Performance studies ..................

7.1 Energy response to single isolated muons ....

7.1.1 Selection of isolated muons ........

7.1.2 Cell response uniformity ..........

7.1.3 Radial layer calibration ...........

7.1.4 Time stability ................

7.2 Energy response to single isolated hadrons ....

7.3 Timing performance with collision data .....

7.3.1 Mean time and time resolution .......

7.3.2 Effect of pile-up on the time resolution ..

7.3.3 Run-to-run differences ...........

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1313 Page 2 of 53 Eur. Phys. J. C (2024) 84:1313

7.4 Electronic and pile-up noise ............

7.4.1 Electronic noise ...............

7.4.2 Pile-up noise ................

7.5 Performance of the tile muon trigger system ...

7.6 Performance of the minimum-bias trigger scin-

tillators ......................

7.7 Summary of performance studies .........

8 Conclusion .......................

References .........................

1 Introduction

ATLAS [1] is a general-purpose detector at the Large Hadron

Collider (LHC) [2] designed to reconstruct events from col-

liding hadrons. It has a forward–backward symmetric cylin-

drical geometry and a near 4πcoverage in solid angle.

ATLAS consists of an inner tracking detector surrounded

by a thin superconducting solenoid providing a 2 T axial

magnetic ﬁeld, electromagnetic and hadronic calorimeters,

and a muon spectrometer. The inner tracking detector covers

the pseudorapidity range of |η|<2.5.1It consists of sili-

con pixel, silicon microstrip, and transition radiation track-

ing detectors. Lead/liquid-Argon (LAr) sampling calorime-

ters provide electromagnetic (EM) energy measurements. A

steel/scintillator-tile hadronic calorimeter covers the central

pseudorapidity range of |η|<1.7. The endcap and forward

regions are instrumented with LAr calorimeters for both the

EM and hadronic energy measurements up to |η|=4.9. The

muon spectrometer (MS) surrounds the calorimeters in the

pseudorapidity range of |η|<2.7 and is based on three large

superconducting air-core toroidal magnets with eight coils

each. The ﬁeld integral of the toroids ranges between 2 and

6 Tm across most of the detector. The muon spectrometer

includes a system of precision tracking chambers and fast

detectors for triggering.

The tile calorimeter (TileCal) is the hadronic barrel

calorimeter sub-detector of ATLAS and provides essential

input to the identiﬁcation of hadronic jets and measurement

of their energy and direction. It also provides information for

triggers, participates in the measurement of the missing trans-

verse momentum carried by non-interacting or not detected

particles, and assists in the identiﬁcation of muons. The Tile-

Cal design provides a standalone energy resolution for iso-

lated pions of σ/E=56.4%/√E(GeV)⊕5.5%, measured

1ATLAS uses a right-handed coordinate system with its origin at the

nominal interaction point (IP) in the centre of the detector and the z-

axis along the beam pipe. The x-axis points from the IP to the centre of

the LHC ring, and the y-axis points upwards. Cylindrical coordinates

(r,φ) are used in the transverse plane, φbeing the azimuthal angle

around the z-axis. The pseudorapidity is deﬁned in terms of the polar

angle θas η=−ln tan(θ/2). Angular distance is measured in units of

R≡(η)2+(φ)2.

at test beams [1], and a linear response with a nonlinearity

tolerance of 2% for simulated jets up to 4 TeVto be sensitive

to the full range of energies expected in the LHC lifetime.

A detailed description of the ATLAS TileCal can be found

in the dedicated Technical Design Report [3]; the construc-

tion, optical instrumentation and installation into the ATLAS

detector are described in Refs. [4,5]; the operation and per-

formance of the TileCal during Run 1 at the LHC are reported

in Ref. [6].

This paper presents the operation and performance of the

TileCal during the LHC Run 2 data-taking period (2015–

2018).2The full ATLAS detector recorded events from

cosmic-ray muons in the period February–July 2015. The

ﬁrst proton–proton (pp) collisions at a centre of mass energy

√s=13 TeV were recorded in April 2015 with a bunch

spacing of 50 ns [7]. Later in 2015 and the following years,

the LHC pp collisions continued to be at √s=13 TeV,but

the instantaneous luminosity and the number of proton col-

lisions per bunch crossing increased and the bunch spacing

decreased to 25 ns. The total integrated recorded (delivered)

luminosity was 145.5 fb−1(157.4 fb−1)[7,8]. Only events

recorded during stable beam conditions and with all ATLAS

sub-detectors fully operational are considered in the analysis.

The corresponding integrated luminosity is 140.1 fb−1[8].

A summary of the LHC beam conditions in Run 2 is shown

in Table 1. Additional special runs with low integrated lumi-

nosity and a low average number of interactions per bunch

crossing, μ, used for commissioning purposes, were taken

with a bunch spacing of 25 ns in 2017 and 2018. During these

years ATLAS also recorded data with lower-energy proton

collisions (√s=5 TeV), and data produced by lead–lead or

xenon–xenon ion collisions. These runs had very low inte-

grated luminosity.

The recorded events are separated into different streams

according to the trigger category for which the event is

selected. Physics streams are composed of triggers that are

used to identify electrons, photons, muons, jets, hadronically

decaying τ-leptons, and missing transverse momentum in

collision data. There are also calibration streams used by

the various sub-detectors for calibration and monitoring pur-

poses.

This paper is organised as follows. Section 2describes

the experimental set-up. The reconstruction and the cali-

bration of physics events are the subjects of Sect. 3.The

calibration streams used by the TileCal for calibration pur-

2In ATLAS, data collected over periods of time spanning an LHC ﬁll

or generally stable conditions are grouped into a ‘run’, while the entire

running period under similar conditions for several years is referred to

as a ‘Run’. Run 1 refers to runs collected in the period 2008–2012. Data

taken within a run are broken down into elementary units called lumi-

nosity blocks, corresponding to about one minute of collision data for

which the detector conditions or software calibrations remain approxi-

mately constant.

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Eur. Phys. J. C (2024) 84:1313 Page 3 of 53 1313

Tabl e 1 Summary of the proton–proton collision data collected in the

four years of Run 2 and used to obtain the results presented in this

paper. The ATLAS analysis integrated luminosity corresponds to the

total integrated luminosity approved for analysis, passing all data qual-

ity requirements ensuring the detector and reconstruction software are

properly functioning [7,8]

2015 2016 2017 2018

Maximum beam energy [TeV] 6.5 6.5 6.5 6.5

Delivered integrated luminosity [fb−1] 4.0 39.0 50.6 63.8

Recorded integrated luminosity [fb−1] 3.6 35.5 46.4 60.0

ATLAS analysis integrated luminosity [fb−1] 3.2 33.4 44.6 58.8

Mean number of interactions per bunch crossing 13 25 38 36

Maximum instantaneous luminosity [1033cm−2s−1] 5 13 16 19

poses were taken in dedicated runs when the beams were

off. As discussed in the paper, they include caesium, laser

and charge injection system (CIS) events. Laser events taken

during empty bunches in the LHC abort gap [2] are used

for timing calibration. Physics events are also used for cal-

ibration purposes. To minimise systematic uncertainties, as

discussed at the end of the section, the procedure used to

reconstruct the experimental energy deposited in the cells is

used in the case of simulated events. The TileCal data sub-

sets used in the hardware-based Level-1 (L1) ATLAS trigger

system are discussed in Sect. 4. The deterioration of the detec-

tor performance due to the radiation exposure is reported in

Sect. 5. These studies allow the amplitude reduction of the

output signals at the end of the LHC and HL-LHC operations

to be estimated. The online and ofﬂine data quality (DQ)

checks applied to the hardware and data acquisition systems

are explained in Sect. 6. The validation of the full chain of

TileCal calibration and reconstruction using physics events

is described in Sect. 7. The results are obtained by analysing

the physics stream containing muons, isolated hadrons, zero-

bias events [9] and jets. The performance of ATLAS trigger

systems based on TileCal information is also discussed. All

results presented in this document are summarised in Sect. 8.

The data used in the studies were collected by the tile

calorimeter sub-detector using the full ATLAS data acquisi-

tion chain. An extensive software suite [10] is used for real

and simulated data reconstruction and analysis, for opera-

tion, and in the trigger and data acquisition systems of the

experiment. In addition to the TileCal, the information of

other ATLAS sub-detectors is used to assist in particle iden-

tiﬁcation, and track, momentum and energy reconstruction.

2 Experimental setup

2.1 Tile calorimeter

The tile calorimeter is a sampling calorimeter consisting of

tiles of plastic scintillator as active material and low-carbon

steel absorber plates. The scintillator tiles are composed of

Polystyrene with a low mass concentration of pTP (1.5%)

and POPOP (0.04%). Further technical details are described

in Ref. [5]. As shown in Fig. 1a, the TileCal surrounds the

LAr calorimeter and ﬁlls the volume from an inner radius

of 2.28 m to an outer radius of 4.23 m. The amount of

material in front of the TileCal at η=0 corresponds to

2.3λ3[1]. The sub-detector thickness at the outer edge of

the tile instrumented region is 9.7λat η=0. Including 1.3λ

from the outer support, the total detector thickness is 11λat

η=0, sufﬁcient to reduce punch-through well below the

irreducible level of prompt decay muons. The calorimeter is

mechanically divided into three segments along the z-axis

(the beam axis): one central long barrel (LB) section that is

5.8 m in length (|η|<1.0), and two extended barrel (EB)

sections on either side of the LB that are each 2.6 m long

(0.8<|η|<1.7).

Each TileCal barrel consists of 64 modules in the angular

direction φallowing full azimuth coverage. Figure 1bshows

a schematic of a TileCal module. In each module, the steel and

scintillator tiles have a thickness of 14 mm and 3 mm respec-

tively. The steel and scintillator tiles are interleaved period-

ically in the longitudinal direction z. The scintillator tiles

are organised along the radius in 11 rows of different sizes,

numbered from 1 to 11 starting from the smallest radius. Each

row corresponds to a group of tiles at the same distance from

the beam axis. Light from the scintillator tiles is collected at

their edges by wavelength-shifting (WLS) ﬁbres, arranged

in pre-shaped opaque plastic ‘proﬁles’ attached to both sides

of the modules and running radially, as shown in Fig. 1b.

The WLS ﬁbres transport the light to photomultiplier tubes

(PMTs). The readout cell geometry is deﬁned by grouping

the ﬁbres from individual tiles on the given PMTs [5]. The

cell layout for LB and EB modules in a plane containing

the beam axis is shown in Fig. 2for z>0. A mirroring of

those in the other direction in zdeﬁnes the three sections

of the calorimeter. Three longitudinal layers are obtained.

3The nuclear interaction length λis deﬁned as the mean path length

required to reduce the ﬂux of relativistic primary protons and neutrons

to a fraction 1/e.

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1313 Page 4 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 1 a Cut-away view of the ATLAS calorimeter system. The Tile-

Cal consists of a barrel and two extended barrel sections. The sections

of the ATLAS liquid-argon (LAr) calorimeters are also indicated. bAn

illustration of the mechanical assembly and optical readout of a single

tile calorimeter module. A total of 256 such modules comprise the full

tile calorimeter. Source tubes are used to circulate a 137Cs radioactive

source contained in a capsule for calibration purposes

In the long barrels, layers A, BC and D cells are made of

three, six and two rows corresponding to depths of 1.5, 4.1,

and 1.8λrespectively. In the extended barrels, layers A, B

and D cells have three, four and four rows corresponding to

depths of 1.5, 2.6, and 3.3λrespectively. The layers allow

the jet energy depositions to be measured at different depths.

The cells have a granularity of η ×φ =0.1×0.1in

the two innermost layers and η ×φ =0.2×0.1inthe

outermost one. As shown in the ﬁgure, the scheme produces

pseudo-projective towers. The total energy in a tower is used

for triggering purposes as discussed in Sect. 2.2.

To correct for energy losses in the gap region between

the TileCal LB, and the EB and the LAr sections, where

many cables and electronics crates from other ATLAS sub-

detectors are located, a special intermediate tile calorimeter

(ITC) system is installed. As shown in Fig. 2, the ITC cells

(D4, C10 and E1–E4) are located between the LB and EB,

and provide coverage in the range of 0.8<|η|<1.6. Some

of the C10 and D4 cells have reduced thickness or special

geometry to accommodate services and readout electronics

for other ATLAS sub-detectors [3,11]. The gap (E1–E2) and

crack (E3–E4) cells are composed of only scintillator media

and are read out by one PMT each. The minimum-bias trig-

ger scintillators (MBTS) [12], used to trigger events from

colliding particles, are also read out by TileCal EB electron-

ics and provide coverage in the range of 2.08 <|η|<3.86

(see Sect. 4.3). Most TileCal cells are read out by two PMTs,

accounting for a total of 9852 readout channels for the 5182

cells.

2.2 Readout electronics

The PMTs and front-end electronics are placed in aluminium

units housed in a steel girder on the outer radius of each mod-

ule. The units can be completely extracted and are therefore

called electronics drawers. Two contiguous connected draw-

ers form a super-drawer. In each module of the LB there are

two super-drawers hosting the electronics of the cells with

η>0 (A-side) and η<0 (C-side) respectively. The elec-

tronics of the cells of each of the modules of the EBs are

located in a super-drawer. In this way, from an electronic

point of view, one identiﬁes four barrels, the LBA and EBA

with η>0 and the LBC and EBC with η<0. There are 45

and 32 channels per super-drawer in the LB and EB, respec-

tively.

Each channel consists of a unit called a PMT block, which

contains the light-mixer, PMT tube and High-Voltage (HV)

divider, and a ‘3-in-1 card’ [13,14]. The card receives the

PMT signal and provides three output signals as follows:

•A shaper with a shaping time of 50 ns shapes the fast

PMT signal to the requirements of the 10-bit, 40M sample

per second ADCs used to digitise the signal at the LHC

bunch crossing rate [15]. The amplitude of the shaped sig-

nal is proportional to the integrated charge of the PMT

pulse. Two linear outputs are produced with a relative

gain of 64 and hence an overall 16-bit dynamic range

using two 10-bit ADCs. These data are digitised contin-

123

Eur. Phys. J. C (2024) 84:1313 Page 5 of 53 1313

500 1000 1500 mm

A3 A4 A5 A6 A7 A8 A9 A10

A1 A2

BC1 BC2 BC3 BC5 BC6 BC7 BC8BC4

D0 D1 D2 D3

A13 A14 A15 A16

B12 B14 B15

D5 D6

C10

0,7 1,0 1,1

1,3

1,4

1,5

1,6

B11 B13

A12

beam axis

0,1 0,2 0,3 0,4 0,5 0,6 0,8 0,9 1,2

2280 mm

3865 mm =0,0

Fig. 2 The layout of the TileCal cells with η≥0, denoted by one or two letters (A–E) plus an integer number. The layer A is closest to the

beamline.The naming convention is repeated for cells with negative η. The long barrel (extended barrel) cells are shown at the left (right)

uously and seven samples centred around the pulse peak,

synchronised with the LHC master clock, are obtained.

The seven samples are stored in a circular pipeline buffer

and transferred to the off-detector electronics when a

Level-1 trigger [9] signal is received. A gain switch is

used to determine which gain information is sent to the

back-end electronics for event processing. By default the

high-gain (HG) signal is used unless any of the seven

samples saturates the ADC, at which point the low-gain

(LG) signal samples are transmitted. The signals from the

digital readout are used to measure physics data as well as

laser and charge injection system calibrations (Sect. 2.3).

•An integrator that receives less than 1% of the PMT cur-

rent digitized by a 12-bit ADC card [16]. The integrator is

a low-pass DC ampliﬁer with six switchable gain settings

and a calibration input. With one of the available gains,

the currents range between 0.01 nA and 1.4 µA over

a time window of 10–20 ms. The integrator readout is

used for calibration and monitoring purposes (Sect. 2.3)

measuring the caesium and physics runs signals. In the

case of physics runs the signals are produced essentially

by inelastic proton-proton collisions at low momentum

transfer, known as minimum-bias (MB) events. Besides

response monitoring, the measurements of MB events

also allow to determine the instantaneous luminosity of

the colliding beams, not discussed in this article.

•A differential signal from the LG output of the shaper is

sent to the adder boards located in the drawer to deter-

mine the total energy in a calorimeter tower used in the

ATLAS trigger (Sect. 4.1). The signals of the D5 and D6

cells are used as input to the tile muon trigger system

(Sect. 4.2). The channels reading the MBTS counters

provide the analogue HG outputs to the corresponding

trigger (Sect. 4.3).

In addition to the PMT signals, the 3-in-1 card also con-

tains an input path for the calibration charge injection system

described in Sect. 2.3.1.

In the case of the digital readout, the DMU (data manage-

ment unit) chips located on the digitiser boards are respon-

sible for organising the digitized samples in packets of data.

There are eight digitiser boards in each super-drawer, and

each one has two DMU devices, so there are 16 DMU devices

per super-drawer. The interface board collects data from all

the digitisers, serialises and transmits them to the back-end

electronics. The integrity of the data received by the back-end

is checked using a CRC (cyclic redundancy check) algorithm.

The back-end electronics are located in the counting room

approximately 100 m away from the ATLAS detector. The

data acquisition system of the TileCal is split into four logical

partitions associated with the super-drawers of the modules

in LBA, LBC, EBA, and EBC. Optical ﬁbres transmit sig-

nals between each super-drawer and the back-end trigger,

timing and control (TTC), and readout driver (ROD [17])

crates. There are a total of four TTC and ROD crates, one for

each logical partition. The ATLAS TTC system distributes

the LHC clock, trigger decisions, and conﬁguration com-

mands to the front-end electronics. If the TTC system sends

the trigger acceptance command to the front-end electron-

ics, the corresponding digital samples for all channels of the

calorimeter are sent to the ROD where the signal is recon-

structed.

The integrator output voltage of each PMT of a module,

proportional to the input charge, is read out sequentially using

a single ADC. The switching between the corresponding 3-

in-1 cards takes place about every 0.1 s. This switching mech-

anism is known to induce noise for a couple of bunch cross-

ings, thus it is done in the LHC abort gap [2]. The ‘SHAFT

board’ [18,19] that is responsible for handling the different

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1313 Page 6 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 3 The signal paths for each of the three calibration systems used by the TileCal. The signal produced by particles from collisions is denoted

by the thick solid line, and the path taken by each of the calibration systems is shown with dashed lines

Tile calibration signals during data-taking, sends the pulse in

order to trigger the sending of the switching command via

TTC to be distributed over the full detector. The digitized sig-

nals are sent to an off-detector readout buffer through CAN

bus.

The front-end electronics of a single super-drawer are sup-

plied by a low-voltage power supply (LVPS) source, which

is positioned in an external steel box mounted just outside

the super-drawer. The HV is set and distributed to each PMT

using dedicated boards positioned inside the super-drawers

next to the front-end electronics.

2.3 Calibration systems

The ATLAS physics performance goals for hadronic jets

measurements require a tolerance of 2% on the non-linearity

of the detector response for jets up to 4 TeV. Jets are clus-

ters of energy deposits in the calorimeter cells, and jet-level

requirements are adopted as conservative requirements for

the cell energy measurements. To achieve the aforementioned

jet performance goals in the ATLAS environment and over an

extended period of time requires calibration of the calorime-

ter cell response with the same 2% precision. Four systems,

the electronic charge injection system, the integrator readout,

the caesium radioactive γ-source system [20] and the laser

system [21] are used, as depicted in Fig. 3. They probe the

three steps in the signal reconstruction of TileCal and MBTS

cells (see Sect. 4.3): production and collection of light in

scintillator tiles and ﬁbres, conversion of light to electrical

signal in PMTs, and calibration of electrical signals in the

electronic readout. The aim is to maintain a uniform and sta-

ble response of the measurements of the energy deposited in

all cells accounting for changes in the optics and electronics.

The calibration systems allow the scale of the cell energy

measurement to be set, as described in Sect. 3, and the deter-

minations obtained using experimental and simulated data

to be compared. The caesium source, MB currents and the

laser system allow to determine the degradation of the Tile-

Cal and MBTS signals due to their exposure to a high radi-

ation level produced during the LHC operations. The latter

is discussed in Sect. 5. Moreover, the three complementary

calibration systems also aid in identifying the source of prob-

lematic channels (see Sect. 6.2). Problems originating strictly

in the readout electronics are seen by both the laser and CIS,

problems related solely to the PMTs are detected exclusively

by the laser system, while caesium sources and MB events

probe also scintillation light production and transport in the

WLS ﬁbres.

2.3.1 Charge injection system

The charge injection system calibrates the front-end electron-

ics. The calibration is performed by injecting a full dynamic

range of input charge signals, similar to what the detector

experiences in physics runs, to the readout electronics of all

channels through the respective 3-in-1 cards. The magnitude

of each injected charge is controlled by a 10-bit digital to

analogue converter (DAC). The DAC’s non-linearity is bet-

ter than 0.4%. The conversion factor is 0.801 pC per DAC

setting count. The DAC setting is increased from 0 to 15 with

a step size of 1 for the HG readout, and varied from 32 to

992 with a step size of 32 for the LG. The two readouts pro-

vide a comprehensive test of the electronics response over

the full range of energies expected during regular running of

the LHC. As discussed in Sect. 2.3.2, a part of this system

is also used to calibrate the gain conversion constant of the

integrator readout (see Fig. 3).

2.3.2 Integrator readout of minimum-bias collisions

The integrator readout measurements of the PMT current

induced by MB pp interactions are used to monitor the vari-

ations of the sub-detector response over time. The current,

proportional to the LHC luminosity, varies with the position

123

Eur. Phys. J. C (2024) 84:1313 Page 7 of 53 1313

of the cell. To avoid saturation and maintain an adequate res-

olution, the integrator gain can be selected by choosing one

of the six predeﬁned resistors that also deﬁne the integration

time. The PMT current is obtained from the ADC voltage

measurement as

I[nA]=ADC [mV]−ped [mV]

Int.gain [M](1)

where ped is the pedestal, measured with circulating beams

before collisions to account for beam background sources

such as beam halo and beam-gas interactions, and Int. gain

is the integrator gain constant determined by an internal cali-

bration circuit with a high precision DAC on the 3-in-1 card.

Dedicated runs are periodically taken to calculate the val-

ues for each of the six gain settings by ﬁtting the linear rela-

tionship between the injected current and measured voltage

response. For an instantaneous luminosity between 1 ×1030

and 3 ×1034 cm−2s−1, the non–linearity of the integrator

response is <1%. The deviation from stability of the inte-

grator gains is better than 0.05% for individual channels and

better than 0.01% on average.

2.3.3 Caesium system

The caesium system employs three 137Cs radioactive γ-

sources, one for the LB, one for the EBA and one for the EBC

modules [20]. A hydraulic system moves the source through

the calorimeter using a network of stainless steel tubes paral-

lel to the beam line running through small holes in each tile

scintillator (see Fig. 1b). It produces 0.662 MeV photons that

generate scintillation light in each tile. The average activity

of the three sources was approximately 323 MBq in March

2015 and dropped to approximately 296 MBq in December

2018 (about −2.3% per year). To collect a sufﬁcient signal,

the electrical readout of the caesium calibration is performed

using the integrator readout path (see Fig. 3). The readout

employs a resistor of 28.81 Mwith corresponding integra-

tion time of 13.9 ms. The caesium system allows the cells’

response to be equalised and the combined variations of the

optical components and the PMTs to be monitored.

2.3.4 Laser system

The laser calibration system consists of a single laser source,

located off detector, able to produce controlled short light

pulses that are simultaneously distributed by optical ﬁbres to

the photocathode of all 9852 PMTs. During the LHC long

shutdown before the start of Run 2, a new laser system [21,22]

was developed to correct shortcomings in electronics and

light monitoring of the ﬁrst system used during Run 1, which

in turn resulted in an improved long term reliability. The

new system has been used since the beginning of Run 2. The

intrinsic stability of the laser light was found to be within 3%,

so to measure the PMT gain variations to a better precision

using the laser source, the response of the PMTs is normalised

to the signal measured by a dedicated photodiode. Its stability

is monitored by an α-source and, in 2015, the variation over

one month was shown to be 0.5%, and the linearity of the

associated electronics response was within 0.2%. As shown

in Fig. 3, the laser system monitors the PMTs and electronic

components of the digital readout. Since the optical ﬁbres

have the same length, the system is also used to equalise the

timing of the digitizers before absolute time calibration with

physics events (see Sect. 3.1.1).

3 Reconstruction and calibration of signals in physics

events

To reconstruct physics events, the energy deposited in a cell

is obtained by summing the energy measurements from the

connected PMT channels, with each channel energy Egiven

E[GeV]= A[ADC]

CADC→pC ×CpC→GeV ×CCs ×CMB ×CLas .

(2)

The signal amplitude Ais determined by using the seven

signal samples of the digital readout (Sect. 2.2) as discussed

in Sect. 3.1. The calibration constant CADC→pC converting

the signal from ADC channel units to pC units is estimated

using the CIS system as reported in Sect. 3.2. The factor

CpC→GeV was determined by measuring the response of the

sub-detector to electrons with test beams (TBs) (Sect. 3.3).

It allows the signals to be expressed in GeV and the experi-

mental energy determinations to be compared with the ones

obtained using simulated events. The factors CCs,CMB and

CLas, discussed in Sects. 3.4,3.5 and 3.6 respectively, are

used to monitor the stability of the responses and provide

corrections for each channel during the ATLAS operations.

The procedure is described in Sect. 3.7. In the case of simu-

lated events, the cell energy deposited is also obtained using

Eq. (2) with the CCs,CMB and CLas constants being equal to

1. The procedure is discussed in Sect. 3.8.

3.1 Signal amplitude

The optimal ﬁltering (OF) algorithm is used to reconstruct

the amplitude Ain units of ADC counts, the time offset τ

(phase) relative to the 40 MHz clock and the pedestal, ped,

of the pulse of the shaped PMT signal [23,24]. The method

linearly combines the seven signal samples Si,i=1, ..., 7

of the digital readout, which are read with 25 ns spacing:

123

1313 Page 8 of 53 Eur. Phys. J. C (2024) 84:1313

Time [ns]

-60 -40 -20 0 20 40 60 80 100 120

Normalised signal height

0.2

0.4

0.6

0.8

1Low gain

High gain

ATLAS

Fig. 4 The reference pulse shapes for high gain (dotted curve) and low

gain (solid curve), shown in arbitrary units [11]

n=7



i=1

aiSi,Aτ=

n=7



i=1

biSi,ped =

n=7



i=1

ciSi.

(3)

The coefﬁcients ai,biand ciare optimised using TB data

to minimise the bias on the reconstructed quantities intro-

duced by the electronic noise (see Sect. 7.4.1)[24]. The

normalised pulse shape function, taken as the average pulse

shape from test beam data, is used to determine the coef-

ﬁcients. As shown in Fig. 4, separate functions are deﬁned

for HG and LG modes. The pulse shape and coefﬁcients are

stored in a dedicated database. For each sample, gain and

phase there are ﬁve coefﬁcients: the three (ai,biand ci) that

appear in Eq. (3) and two additional for the calculation of

quality factors. In total there are 70 coefﬁcients per channel

and the constants are the same for all the channels with the

same timing. Pile-up effects are not considered. A total of

2001 phase values in the range −100 ns and +100 ns with

steps of 0.1 ns are considered, resulting in 140070 constants

stored in the database.

The values of A,τand ped are obtained by solving the

set of equations in Eq. (3). The signal reconstruction is per-

formed twice: i) in real time by the RODs (referred to as

online) for use in the trigger, and ii) after the data have been

recorded (referred to as ofﬂine) for use in the data analysis.

The expected time of the pulse peak is calibrated such

that, for particles originating from collisions at the interaction

point, the pulse should peak at the central (fourth) sample,

synchronous with the LHC clock. The reconstructed value

of τrepresents the time offset in nanoseconds between the

expected pulse peak and the time of the actual reconstructed

signal peak, arising from ﬂuctuations in particle travel time

and uncertainties in the electronics readout (see Sect. 3.1.1).

There are two versions of the optimal ﬁltering algorithm,

iterative and non-iterative. The coefﬁcients ai,biand ciare

functions of the pulse’s true time offset relative to the 40 MHz

clock. This time offset is known only approximately a pri-

ori before the reconstruction. The iterative OF method takes

the time of the maximum sample as an initial value of the

time offset. In the subsequent iterations, the input time offset

is taken to be equal to τcalculated in the previous itera-

tion. The algorithm converges to the actual time offset value

with an accuracy better than 0.5 ns in the absence of pile-

up pulses (see Sect. 7.4.2). Typically, three iterations are

needed to converge. This method is used when reconstructing

events occurring asynchronously with the LHC clock, such

as cosmic-ray muons. In the case of LHC physics events,

with an increasing number of events per bunch crossing, the

non-iterative method is used both online and ofﬂine, for being

faster and more robust against pile-up. The out-of-time pile-

up, see Sect. 7.3.2, can lead to a reconstructed time offset

value far from the expected one, biasing the energy measure-

ment when the iterative method is used. The non-iterative

OF method performs only a single iteration of the optimal

ﬁltering method and uses as input the time offset determined

for each TileCal channel from prior timing calibration runs.

This is the time offset expected for in-time pulses from the

collisions of interest. By forcing the time offset to its prede-

termined expected value, the non-iterative optimal ﬁltering

method better reconstructs the energy of the in-time pulse

from a collision of interest in the presence of out-of-time

pile-up. This method is also more robust against electronic

noise for very low amplitude signals.

In real time, or online, the digital signal processor (DSP)

in the ROD performs the signal reconstruction using the

OF technique and provides the channel energy and time to

the High-Level Trigger [9]. The conversion between signal

amplitude in ADC counts and energy units of GeV is done

by applying channel-dependent calibration constants that are

described in the following sections. The DSP reconstruction

is limited by the use of ﬁxed point arithmetic, which has a

precision of 0.0625 ADC counts (approximately 0.75 MeV

in HG), and imposes precision limitations for the channel-

dependent calibration constants. The ofﬂine signal is recon-

structed using the same iterative or non-iterative OF tech-

nique as online but using ﬂoating point arithmetic.

To avoid saturation of the output ROD bandwidth in high

instantaneous luminosity conditions, all LG channels and

only HG channels for which the difference between the

maximum and minimum Siis larger than ﬁve ADC counts

(approximately 60 MeV) have the raw data transmitted from

the ROD for ofﬂine signal reconstruction. Otherwise, the

ROD signal reconstruction results are used for the ofﬂine

123

Eur. Phys. J. C (2024) 84:1313 Page 9 of 53 1313

data processing. The procedure has a negligible impact on

the performance of the energy reconstruction.

3.1.1 Channel timing calibration

To allow for optimal energy reconstruction by the non-

iterative OF method and to enable precise time-of-ﬂight mea-

surement in certain physics analyses, the time difference

between the digitising sampling clock and the peak of the

PMT pulses must be minimised and measured with a preci-

sion of 1 ns. To achieve this, the clock phases in the DMUs in

the front-end hardware (see Sect. 2.2) are adjusted in multi-

ples of 104 ps. The hardware time offset can be set for groups

of six channels.

The initial time calibration for Run 2 was performed using

the relative timing differences per channel relative to the end

of Run 1 measured by the laser system (see Sect. 2.3.4).

The calibration was later reﬁned using beam-splash events

from a single LHC beam utilising the same method as

before the start of Run 1 [11]. Finally, the time calibration

is established with the pp collision data. To avoid possible

bias from non-collision beam backgrounds, only channels

belonging to reconstructed jets satisfying standard quality

criteria [25,26] are considered in each event. Given that the

timing is slightly dependent on the energy deposited in a

cell, in the case of HG signals, the offset is determined in

the range 2 GeV <E<4 GeV. In the case of LG signals

the behaviour is smoother and a broader energy range of

15 GeV <E<50 GeV is chosen. The distributions show

that more than 99.5% of the reconstructed τvalues are found

to be between −10 ns and +10 ns. A non-zero value of the

phase τcauses the reconstructed amplitude to be underes-

timated. A correction (parabolic correction) based on the

phase is applied when the phase is reconstructed within half

the LHC bunch spacing (12.5 ns) and the channel amplitude

is larger than 15 ADC counts, to reduce contributions from

noise [6]. After applying the correction, the reconstructed

amplitudes are found to underestimate the actual signal by

less than 1% in magnitude within time phases of ±10 ns.

Pile-up is included in simulated events, such that the sig-

nal amplitude reconstruction proceeds in the same way for

simulated and real events (see Sect. 3.8).

3.1.2 Channel timing monitoring

As discussed, the time settings in each channel are adjusted

so that the channel signal pulse produced by a collision par-

ticle peaks at the central sample of the digital readout. Two

complementary procedures are developed to monitor the time

calibration. The ﬁrst one exploits the laser calibration events

recorded during the empty bunch crossings of physics runs

with a frequency of about 3 Hz. This tool provides the recon-

structed time as a function of the luminosity block in each

channel. The second tool directly uses physics events. Only

HG signals are monitored. Both tools were systematically

used in all runs and revealed three main types of problems:

the so-called timing jumps, bunch-crossing offsets, and bad

or unstable channels.

In a timing jump, the reconstructed time suddenly changes

for a group of six channels belonging to the same digitiser

board. An example of such a case, identiﬁed by both moni-

toring tools, is shown in Fig. 5. Timing jumps are corrected

by adjusting the corresponding time constants in the affected

period. The observed frequency of timing jumps was much

lower than in Run 1 [6] due to the improved stability of the

LVPS, and they have no direct impact on the overall timing

performance.

The bunch-crossing offsets correspond to the cases where

channels have reconstructed times occasionally deviating by

one or two bunch crossings, i.e. ±25 or ±50 ns. This fea-

ture affects three channels connected to the same DMU [15],

described in Sect. 2.2, and thus, the magnitude of the

observed offsets and affected events are fully correlated

across the three channels. The problem is intermittent and

the number of affected events are typically at the percent-

level. The bunch-crossing offsets are identiﬁed with laser

and physics events. An example is shown in Fig. 6a. In order

to mitigate this problem, the affected channels are ﬂagged in

the conditions database (Sect. 6.4) and a dedicated software

algorithm was developed to identify individual problematic

events in the affected channels and exclude them from further

data processing. Figure 6b compares the reconstructed time

in physics events with and without this algorithm applied.

A signiﬁcant fraction of affected events close to +25 ns is

removed. This algorithm was used during the data repro-

cessing campaign.

Some channels have a distorted pulse shape resulting in

a wrong reconstructed time or they exhibit instabilities in

the reconstruction. This is typically caused by a malfunc-

tioning or damaged hardware component in a given channel.

A special ﬂag is assigned to such channels to prevent the

incorrect time from further propagation in the object recon-

struction and subsequent data analysis. In total, 35 channels

were ﬂagged as having bad or unstable timing at the end of

Run 2.

3.2 The conversion factor from ADC counts to pC

The CIS system described in Sect. 2.3.1 is used to deter-

mine the factor CADC→pC. Runs are typically taken daily for

both gains in the absence of colliding beams. The procedure

of injecting charges, sampling the analogue pulse, and mea-

suring the ﬁtted amplitude is repeated as the DAC value is

increased. The relation between the reconstructed amplitude

(in ADC counts) discussed in Sect. 3.1 and injected charge

(in pC) is obtained by performing a single parameter linear ﬁt

123

1313 Page 10 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 5 a The reconstructed time of laser events as a function of the

luminosity block. Data from six channels belonging to the same digi-

tizer are superimposed. The timing jump (from the expected mean time

of 0 ns indicated by the dashed line) lasted the entire duration of the

run and all events are centered around +15 ns. bThe 2D histogram

shows the average channel time in physics events on a colour scale as a

function of module number (x-axis) and channel number (y-axis)

Fig. 6 a The reconstructed time of laser events as a function of the

luminosity block. Data from three channels belonging to the same DMU

are superimposed. The majority of events, centred around zero, are well

timed in. The events affectedby the the bunch-crossing offset are centred

at +25 ns. bThe reconstructed time in physics events in the same three

channels with (corrected) and without (original) applying the algorithm

mitigating the bunch-crossing offset events. The algorithm signiﬁcantly

reduces events centred around +25 ns

123

Eur. Phys. J. C (2024) 84:1313 Page 11 of 53 1313

2015

Oct

2016

Jan

2016

Apr

2016

Jul

2016

Oct

2016

Dec

2017

Apr

2017

Jul

2017

Oct

2017

Dec

2018

Apr

2018

Jul

2018

Oct

CIS Calibration [ADC count/pC]

79.5

80.5

81.5

82.5

83.5

0.7%⎪↵⏐Absolute Systematic Uncertainty

9710 channel average (RMS = 0.04%)

Typical Channel (Long Barrel, C-Side) (RMS = 0.03%)

ATLAS

Tile Calorimeter

HG ADCs

Aug 2015-Nov 2018

(a)

2015

Oct

2016

Jan

2016

Apr

2016

Jul

2016

Oct

2016

Dec

2017

Apr

2017

Jul

2017

Oct

2017

Dec

2018

Apr

2018

Jul

2018

Oct

CIS Calibration [ADC count/pC]

1.26

1.27

1.28

1.29

1.3

1.31

1.32

1.33

0.7%⎪↵⏐Absolute Systematic Uncertainty

9771 Channel Average (RMS = 0.03%)

Typical Channel (Long Barrel, C-Side) (RMS = 0.02%)

ATLAS

Tile Calorimeter

LG ADCs

Aug 2015-Nov 2018

(b)

Fig. 7 The charge injection system constants (CADC→pC)forthea

high-gain and blow-gain ADCs, as a function of time, observed dur-

ing the entire Run 2 (between CIS calibration runs taken on August

2015 and November 2018). Values for the average over all channels

and for one typical channel are shown. The RMS values indicate the

ﬂuctuations present in calibrations. In addition, there is a 0.7% system-

atic uncertainty present in individual calibrations, represented by the

shaded error band. Only good channels not suffering from damaged

components relevant to the charge injection calibration are included

for speciﬁc ranges of charge (3–10 pC for HG ADCs, 300–

700 pC for LG ADCs).4The slope of the linear ﬁt yields the

calibration constant CADC→pC for the ADC of each channel

in units of ADC/pC. For channels where the calibration con-

stant differs by more than 1.0% from the previous value, the

constant is updated for the energy reconstruction.

Figure 7shows the stability of the charge injection con-

stants, as a function of time, observed during the entire Run 2

for the HG and LG ADC channels. There is a 0.7% sys-

tematic uncertainty present in individual calibrations, repre-

sented by the shaded error band. This uncertainty comes from

the observed peak output amplitudes and is taken as charac-

teristic of the channel-to-channel variation from this source,

prior to any calibration. It can be seen that the detector-wide

mean CIS constant over all non-problematic channels falls

within the systematic error band of the typical channel plot-

ted, throughout Run 2. Figure 8shows the change in the aver-

age CIS constant in per cent for every channel in the detector

between August 2015 (at the beginning of Run 2) and Octo-

ber 2018 (at the end of Run 2). In all ﬁgures shown, channels

that are unresponsive or have CIS constants that ﬂuctuate

run-to-run are not included. For the duration of Run 2 the

CIS constants of only 32 channels in the detector changed by

more than ±4%.

4The lower limit of each ﬁt range is chosen to ensure the precision

of each injection to be at least 0.4%, which ensures a measured error

value of less than 0.5%. The upper limit on the HG ﬁt range eliminates

injections that saturate the ADC, as the amplitude of charge values

>10 pC are all read out at 1023 ADC.

Variation of CIS Calibration Constants [%]

4−3−2−1−01234

Number of ADC Channels

ATLAS

Tile Calorimeter

Aug 2015 - Oct 2018

Mean = 0.086%

RMS = 0.353%

Fig. 8 Variations in the detector-wide CIS constants (in per cent)

between August 2015 and October 2018. The RMS variation is approx-

imately 0.35%. Only good channels not suffering from damaged com-

ponents relevant to the charge injection calibration are included. The

ﬁrst and last bins contain underﬂow and overﬂow, respectively

3.3 Signal measurement in GeV

The electromagnetic (EM) scale calibration constant CpC→GeV,

converting the cell signals measured as an electric charge

in pC to GeV, is obtained from the measured amplitude

A[ADC]of the signal produced by electrons at dedicated

test beam campaigns using 11% of the production mod-

ules [11,27]:

CpC→GeV =A[ADC]

CADC→pC ×Ee

(4)

123

1313 Page 12 of 53 Eur. Phys. J. C (2024) 84:1313

At TBs, the electrons hit the centre of the A cells at an angle

of 20◦relative to the cell surface normal. The energy Eeof

the incident electron is completely deposited in the hit cell.

The calibration constant CADC→pC, obtained using CIS mea-

surements, allows the measured amplitude in ADC counts

to be converted to pC (Sect. 3.2). The value of CpC→GeV

amounts to 1.050 ±0.003 pC/GeV with an RMS spread of

(2.4±0.1)%.

To transport to ATLAS the value of the EM scale deter-

mined at the TB, at the start of Run 2, the response of all Tile-

Cal cells was equalised using the same procedure as before

the electron runs at the TB and the start of Run 1 [6]. In

February 2015, ahead of the start of collisions, the HV of each

PMT was adjusted so that the integrator response to the 137Cs

source in the PMTs was equal to that observed before the start

of Run 1 and also equal to the response measured during TB

campaigns. Corrections were applied taking into account the

activity decrease of the 137Cs source, which is about 2.3%

per year. After the equalisation, the HV applied to the PMTs

was kept unchanged during the entirety of Run 2. Due to

light attenuation, the scintillator tile response depends on the

impact point position of the particle on the tile, as well as on

the tile size. Correction factors are applied for each layer of

the calorimeter to account for both effects. Those values are

determined from TB data, measuring the response to muons

impinging on the calorimeter with a direction parallel to the

z-axis (beam axis in Fig. 2), and from the measurements

obtained using a 90Sr source [27].

3.4 Monitoring of the PMT, tile and ﬁbre response with the

caesium system

Since a caesium scan [20] needs a pause in the pp collisions

of at least six hours, this calibration cannot be performed

very often. Moreover, during the LHC technical stop at the

beginning of the data-taking period in 2016, a few traces of

liquid coming from the caesium hydraulic system were found

in the detector cavern. Since then until the end of Run 2,

caesium scans were restricted to the end of year technical

stops due to risk of the liquid leak and were performed only

a few times per year in Run 2.

Figure 9a shows the response deviation from the expected

value, RCs in per cent, as a function of time, averaged over

all cells in a given radial layer. These drifts are a combination

of different sources (scintillator tiles, WLS ﬁbres and PMTs),

as detailed in Sect. 5.1 discussing the radiation effects to the

detector. Due to higher radiation exposure, the most affected

cells are located at the inner radius in layer A. Figure 9bshows

the difference in cell responses recorded over the period of

Run 2 (between February 2015 and October 2018), for cells

located at different η. It can be seen that the degradation

is not uniform across η, an effect of the different radiation

doses received. At the end of Run 2, the response of the most

irradiated cells in layer A had drifted downward by 18%,

while central cells in outer layer D drifted up by 2%.

The data from Fig. 9b is one of the inputs for the combined

channel calibration, as detailed in Sect. 3.7. In each channel,

the calibration constant, CCs in Eq. (2) is related to RCs

according to

CCs =1+RCs.(5)

The precision of the caesium calibration in a typical cell

is approximately 0.3%. For cells on the extreme sides of a

partition, the precision is 0.5% due to larger uncertainties

associated with the source position. The precision for the

narrow C10 and D4 ITC cells is 3% and 1%, respectively.

3.5 Monitoring of the PMT, tile and ﬁbre response with

minimum-bias collisions

Corrections based on MB measurements are applied during

the reprocessing of the data. Since the MB response is pro-

portional to the instantaneous luminosity, the cell response

to MB events is normalised to luminosity measurements

obtained using the inner tracker [8]. The response deviation

in each cell type, RMB, is determined relative to a nomi-

nal value and then turned into a calibration constant, CMB in

Eq. (2), as described in Sect. 3.7. The precision of the mea-

surements is approximately 1.2%. As an example, Fig. 10

shows the variation of the average response to MB events for

the cells in the gap/crack region of the Extended Barrel as a

function of time. These cells are exposed to high radiation

doses.

3.6 Monitoring of the PMT response with the laser system

The laser system is used to monitor the variation of the PMT

response [21]. Deviation in each channel response relative

to its nominal value, RLas, is translated into a calibration

constant, CLas in Eq. (2) using the equation

CLas =1+RLas.(6)

The laser calibration runs are usually taken daily for both

gains, in the absence of colliding beams (standalone runs).

To address the fast drift of PMT response caused by the large

instantaneous luminosity, the laser calibration constants were

updated every 1–2 weeks.

Figure 11 shows the mean of the RLas values for each cell

type (average over φ) measured with the laser system during

the entire pp collisions period in 2018. The most affected

cells are those located at the inner radius and in the gap and

crack region with down-drift up to 4.5% and 6%, respectively.

Those cells are the most irradiated and their readout PMTs

experience the largest anode current for a given luminosity.

123

Eur. Phys. J. C (2024) 84:1313 Page 13 of 53 1313

100

120

140

160

]

-1

Integrated Delivered Luminosity [fb

01/01/15 01/01/16 31/12/16 31/12/17 01/01/19

Date [dd/mm/yy]

12−

10−

8−

6−

4−

2−

Average Response Variation to Cs [%]

Layer A

Layer BC

Layer D

ATLAS

Tile Calorimeter

Run 2

(a)

1.5−1−0.5−0 0.5 1 1.5

20−

15−

10−

5−

Average Response Variation to Cs [%]

Layer A

Layer B

Layer D

ATLAS

Tile Calorimeter

Feb 2015 - Oct 2018

(b)

Fig. 9 a The average response variation of the TileCalcells to the 137 Cs

source relative to the expected value, RCs, as a function of time. The

average runs over all cells in three radial layers. The increasing response

corresponds to the periods without collisions. The LHC delivered inte-

grated luminosity is shown by the shaded area. bThe average response

variation of the TileCal cells to the 137Cs source relative to the expected

value, RCs, after Run 2 data-taking, as a function of the cell position

in η, for three different layers

Fig. 10 The variation of the

average response to MB events

RMB for the cells in the

gap/crack region of the extended

barrel as a function of time. This

quantity is derived relative to the

luminosity measured using the

inner tracker. The error bars

correspond to the RMS of all the

response distributions. The

results are normalised to the

values measured in the ﬁrst run

of Run 2 (16th July 2015). The

integrated luminosity delivered

by the LHC is shown by the

shaded area

03/07/15 23/01/16 14/08/16 05/03/17 25/09/17 17/04/18 07/11/18

Date [dd/mm/yy]

40−

35−

30−

25−

20−

15−

10−

5−

Average Response Variation [%]

100

120

140

160

]

-1

Integrated Delivered Luminosity [fb

ATLAS

Tile Calorimeter

= 13 TeVs

Run 2 Data,

Gap/Crack Cells

|<1.1ηE1: 1.0<|

|<1.2ηE2: 1.1<|

|<1.4ηE3: 1.2<|

|<1.6ηE4: 1.4<|

The EB partitions experience larger current draws than the

LBs due to higher exposure. Figure 12 shows the time evolu-

tion of the mean response variation in the PMTs of each layer

observed during the entire Run 2. The PMT response exhibits

a correlation with the LHC operation, as detailed in Sect. 5.1.

For data taken in 2015 and 2016, the laser calibration con-

stants were calculated and applied for channels with PMT

response variations larger than 1.5% (2%) in the LB (EB). In

2017 and 2018, the thresholds on PMT response variations

were removed and all channels were corrected weekly. The

total statistical and systematic errors in the laser calibration

constants depend on the integrated luminosity Land are

found to be σ/CLas =0.16%(0.32%)·L[fb−1]⊕0.5% for

the LB (EBs).

3.7 The calibration procedure

During Run 2, data from the four sub-detector calibration sys-

tems CIS, caesium, integrator readout and laser, were used to

correct for the response variation of the different calorime-

ter components. The detector calibration constants entering

in Eq. (2)CADC→pC,CCs,CMB and CLas arestoredina

database consulted during online and ofﬁne energy recon-

struction and are deﬁned within a time interval or a range of

run numbers or a range of luminosity blocks where they are

applicable.

The calibration activities started in 2015 with the equali-

sation of the detector calibration through HV adjustments to

all PMTs and the channel timing calibration, as discussed in

123

1313 Page 14 of 53 Eur. Phys. J. C (2024) 84:1313

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

1.2

1.3

1.4

1.6

ATLAS

Tile Calorimeter

-3.24

-3.34

-3.28

-3.39

-3.27

-3.83

-3.75

-3.80

-4.06

-4.09

A10

-1.70

-1.73

-1.66

-1.81

-1.82

-1.72

-1.89

-2.52

-2.31

-1.70

-1.73

-1.66

-1.81

-1.82

-1.72

-1.89

-2.52

-.34

-.31

-.26

-1.26

-3.55

A12

-4.41

A13

-4.00

A14

-2.80

A15

-1.89

A16

-2.87

B11

-2.70

B12

-1.97

B13

-1.31

B14

-.94

B15

-2.54

C10

-2.16

-2.00

-.78

-4.61

-5.93

-4.62

-3.92

<-5.0% -2.5% 0% 2.5% >5.0%

Fig. 11 The mean response variation RLas in the PMTs for each cell

type, in percent, averaged over φ, observed during the entire pp colli-

sions data-taking period in 2018 (between standalone laser calibration

runs taken on 18 April 2018 and 22 October 2018). For each cell type,

the response variation is deﬁned as the mean of a Gaussian function

ﬁt to the response variations in the channels associated with given cell

type. A total of 64 modules in φare used for each cell type, with the

exclusion of known bad channels

]

-1

Integrated Delivered Luminosity [fb

100

120

140

160

Date [dd/mm/yy]

01/01/16 01/07/16 31/12/16 02/07/17 31/12/17 02/07/18

Average PMT Response Variation [%]

6−

4−

2−

6Layer A

Layer BC

Layer D

Yearly start of pp collisions

ATLAS

Tile Calorimeter

Run 2

Fig. 12 The mean response variation RLas in the PMTs for each

layer, as a function of time, observed during the entire Run 2 (between

standalone laser calibration runs taken on 17 July 2015 and 22 October

2018). For each layer, the response variation is deﬁned as the mean

of a Gaussian function ﬁt to the response variations in the channels

associated with given layer. Known bad channels are excluded. The

LHC integrated delivered luminosity is shown by the shaded area

Sects. 3.3 and 3.1.1, respectively. Afterwards, regular updates

of the caesium, laser and CIS calibration constants in Eq. (2)

were performed during the data-taking period for prompt

data processing. The laser system was used to calibrate the

response of the PMTs and readout electronics by frequently

updating the CLas factors calculated relative to the previous

caesium scan. When the rare caesium scans were performed,

the values of the CCs constants were updated, the laser ref-

erence signals for each PMT were written to the database

and the laser constants were reset to 1. This procedure per-

mitted the overall response of the detector to be calibrated

with a negative offset smaller than 1% in absolute value for

most of the detector cells, which have not been harmed by

signiﬁcant radiation damage. Due to the accumulated radia-

tion exposure, since 2016 it is not possible to correct for the

response reduction of the most exposed cells with an accu-

racy down to 2% by relying exclusively on the sparse caesium

scans and laser measurements.

Every year, the data recorded by the ATLAS detector are

reprocessed to apply ﬁner calibrations and improved algo-

rithms in the ATLAS reconstruction chain, as well as to rec-

tify any missed or previously unattainable corrections dur-

ing prompt data processing. In particular, in the LHC Long

Shutdown 2 at the end of Run 2, the full Run 2 data set

was reprocessed. For the TileCal calibration, the reprocess-

ing campaigns also included the determination of the cali-

bration constant CMB appearing in Eq. (2) from the analysis

of the acquired collision data. The correction was introduced

to address the residual miscalibration effects due to scintil-

lator and WLS ﬁbre degradation beyond the PMT response

variation, more precisely calibrated with the laser system, as

123

Eur. Phys. J. C (2024) 84:1313 Page 15 of 53 1313

follows:

CMB =1+RMB −RLas.(7)

The cell response variations to MB events, RMB, are cal-

culated yearly relative to their nominal values at the begin-

ning of the data-taking campaign and close to the Cs scan,

when CMB is reset to 1. These are computed as an average of

the values per cell type since the optics response variations

are found to be similar accross the different detector mod-

ules. They were introduced for the E-cells and a few cells

mostly in layer A (A13 in 2016, A12 and A13 in 2017, and

A12, A13, A14 and especially narrow C10 in 2018), which

show larger response variations during LHC operation.

3.8 Signal reconstruction of simulated events

In physics analysis, the measured energy is compared with

that obtained by analysing simulated events. The ATLAS

Monte Carlo (MC) simulation [28] relies on the Geant4

toolkit [29] to model the detector and the interactions of par-

ticles with the detector material. The MC process is divided

into four steps: event generation, simulation, digitisation,

and reconstruction. Various event generators are used for

the ATLAS physics analyses and performance studies, see

Sect. 7. During Run 2, ATLAS used the ‘FTFP_BERT_ATL’

physics model to describe the hadronic interactions with mat-

ter, where at high energies the hadron showers are modelled

using the Fritiof string model. The Bertini intra-nuclear cas-

cade model is used for lower-energy hadrons [30]. The tran-

sition between those two models takes place in the energy

region 9–12 GeV [31].

The input to the digitisation is a collection of hits in the

active scintillator material, characterised by energy, time and

position. The amount of energy deposited in the scintillator

is divided by the calorimeter sampling fraction to correct

for energy deposited in the inactive material. To obtain the

channel energy at the electromagnetic scale [32] the sampling

fraction is obtained by simulating electrons with the same

kinematics features as the ones used at the TBs (Sect. 3.3).

In the case of a cell read out by two PMTs, the energy is

shared between the two PMTs according to the distance in

rφof each hit from the edges of the cell. Moreover, due to cell

geometry and the presence of the caesium system pipes, the

cell energy response has an azimuthal dependence, measured

using W→μν events in the 2012 pp collision data [33].

This dependence is implemented in the MC simulations.

To follow the same procedure used in the reconstruction

of experimental events, in the digitisation step, the channel

energy in GeV is converted into its equivalent charge using

the electromagnetic scale constant measured at TBs. The

charge is subsequently translated into the signal amplitude

in ADC counts using the corresponding calibration constant

as explained in Sect. 3.2. The amplitude is convoluted with

the pulse shape and digitized every 25 ns as in real data.

The effects of the electronics are emulated and added to the

digitized samples as described in Sect. 7.4.1. The generation

of the simulated event samples includes the effect of multi-

ple pp interactions per bunch crossing, and the effect on the

detector response as described in Sect. 7.4.2, due to bunch

crossings close to the one containing the analysed interaction.

4 Trigger signals

A two-level trigger system [9] was used by ATLAS in Run 2

to reduce the event rate from a maximum raw rate of 40 MHz

to 1 kHz, which is written to disk. The Level-1 trigger is

implemented in hardware and uses a subset of calorimeter

and muon sub-detector information to accept events at a rate

below 100kHz. The data are searched for signatures such as

large energy deposits (Level-1 calorimeter trigger) or high-

pT muon tracks (Level-1 muon trigger). This is followed by

a software-based high-level trigger (HLT), implemented in a

large dedicated computer farm adjacent to the cavern, which

reduces the accepted event rate to 1kHz on average depend-

ing on the data-taking conditions. The TileCal data subsets

used in the L1 trigger are discussed in the next sections. As

discussed in Sect. 4.3, information collected by the TileCal

is also used to select events during low luminosity LHC runs.

4.1 Level-1 calorimeter trigger

The Level-1 calorimeter trigger exploits the tower structure

in the η-φplane of the TileCal (see Fig. 1b) and the LAr

calorimeter. The TileCal information used in L1 comes from

the adder boards, referred to in Sect. 2.2. These boards sum

the 3-in-1 card low-gain analogue signals of the six channels

connected to cells with the same ηand φin the layers A, BC

and D of the LB and in the layers A, B and D of the EB.

4.2 Tile muon trigger

The tile muon trigger uses information from the TileCal out-

ermost layer cells (D cells) of the EB to reduce the rate of

the ATLAS L1 muon trigger [1] background events due to

low momentum protons emerging from the endcap toroid and

beam shielding. Figure 13 shows that muons arising from pp

collisions with 1.0<|η|<1.3, cross the D5 and D6 cells

of the TileCal extended barrel before reaching the endcap

muon chambers (TGC) [1]. This is not the case for back-

ground events. The requirement of a coincidence between

the TileCal and the muon sub-detector signals reduces the

background rate.

The tile muon trigger is based on the information of 2 ×

64 TileCal azimuth sectors, each corresponding to one EB

123

1313 Page 16 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 13 Schematic of a partial

longitudinal cut of the ATLAS

detector showing the ηregion

covered by the tile muon trigger

system. The azimuth angle

coverage is 0 ≤φ<2π.The

sub-systems not used in the tile

muon trigger are sketched for

completeness and their drawings

do not represent the accurate

geometry

module, and 2 ×24 TGC chamber sectors. To provide the

TileCal and TGC sector matching, a tile muon digitiser board

(TMDB) is required to process the D5 and D6 signals from

eight TileCal modules and interface with three Level-1 TGC

sector logic blocks [34,35] (Fig. 14). To cover the entire

detector, 16 TMDBs housed in a VME 9U crate in the ATLAS

service cavern are required.

For each TileCal sector, the TMDB provides four energy

values, one for each of the two PMT’s of the D5 and D6 cells.

The analogue signals are digitized at a frequency of 40 MHz

and a window of seven samples in time is used to represent

the readout pulses. The method adopted for energy estima-

tion is based on the Matched Filter (MF) [36], which has suc-

cessfully been tested for the TileCal signals [37]. Unlike the

OF method discussed in Sect. 3.1, the MF approach aims at

maximizing the signal-to-noise ratio for muon signals while

increasing the detection efﬁciency. For the TMDB, the MF

employed for each channel is implemented by a 7-coefﬁcient

digital ﬁlter where the signal energies are estimated by per-

forming an inner product between the MF coefﬁcients and

the incoming time samples in ADC counts. The presence of a

signal produced by a muon in the D5, in the D6, and in the D5

and D6 cells is obtained by comparing the four energy deter-

minations with the corresponding threshold values. The MF

coefﬁcients and threshold values are loaded in the TMDB.

The board transmits the ηand φcoordinates of the identiﬁed

cells to the three TGC logic blocks through three Gigabit

links (GLink). The information is also transmitted to neigh-

bour receiver boards to accommodate the non-perfect match-

Fig. 14 The basic architecture of the tile muon trigger system

ing between the eight TileCal modules and the three muon

sector logic blocks. Each TGC sector Logic board performs

the coincidence. The performance of the tile muon trigger

systems is discussed in Sect. 7.5.

4.3 Minimum-bias trigger scintillators

During low luminosity LHC runs, the MBTS system allowed

the online selection of events with the highest efﬁciency

and the lowest possible bias [12]. The system consists of

2 cm thick polystyrene scintillator counters made by PS-

PTP-POPOP polyethylene located on both A- and C-sides

of the ATLAS detector at a distance of 3.6 m from the inter-

action point. Each side is composed of eight inner counters

(2.76 ≤|η|<3.86) and eight outer counters (2.08 ≤|η|<

123

Eur. Phys. J. C (2024) 84:1313 Page 17 of 53 1313

Inner Layer

Outer Layer

Fig. 15 The layout of one side of the MBTS sub-detector in the period

2015–2017. The energy deposited in each sensitive region is read by one

readout channel. Due to the large reduction of the scintillator response,

the number of sensitive regions in the outer layer was doubled in 2018

2.76) instrumented with WLS ﬁbres embedded in grooves at

the edges of the counters. The light going through the ﬁbres

is directed to PMTs [5] where the signal readout is done

using the same electronics as the TileCal cells (Sect. 2.2).

The signals from the shaper output (Sect. 2.2) are fed into

leading edge discriminators and sent as 25 ns NIM pulses

to the Central Trigger Processor [38]. As shown in Fig. 15,

in the period 2015–2017 there are twelve readout channels

in total in each side. Each inner counter is connected to one

PMT and adjacent pairs of outer counters are read by a single

PMT grouping the corresponding WLS ﬁbres.

Due to the high irradiation dose, the performance of

the scintillators, ﬁbres and PMTs of the MBTS degraded

throughout Run 2 and a large signal reduction was observed.

This is discussed in detail in Sect. 5.3. The MBTS efﬁcien-

cies, observed at the beginning of Run 2 and after two years

of LHC operations, are reported in Sect. 7.6. Due to the

very large degradation of the response, the MBTS counters,

installed before the start of Run 2, were replaced at the begin-

ning of Run 3.

5 Radiation exposure effects

The LHC operation affects the performance of the TileCal

and of the MBTS. Prolonged exposure to intense radiation

causes a decrease in the optical transmission of the scin-

tillator tiles and of the WLS ﬁbres, inducing a decrease

in pulse height which may result in a deterioration of the

03/07/15 23/01/16 14/08/16 05/03/17 25/09/17 17/04/18 07/11/18

Date [dd/mm/yy]

15−

10−

5−

Average Response Variation [%]

100

120

140

160

]

-1

Integrated Delivered Luminosity [fb

ATLAS

Tile Calorimeter

= 13 TeVs

Run 2 Data,

|<1.3ηA13: 1.2<|

Laser

Minimum Bias

Cesium

Fig. 16 The average response variation of A13 cells measured by the

laser, caesium and minimum-bias integrator systems as a function of

time during the entire Run 2. Known bad channels are excluded. As

discussed in the text the response behaviours are connected to the LHC

delivered luminosity shown by the shaded area

energy resolution of the detector. Simulation studies show

that a 50% reduction of the cell light yield (from the typ-

ical 70 photoelectrons/GeV) can be acommodated with-

out signiﬁcant impact on the energy resolution for 20 and

160 GeV muons [39]. On the other hand, the accumulated

charge affects the gain of the PMTs (see Sect. 5.1). The laser

measurements allow the effects of the PMTs on the calorime-

ter response to be determined while the caesium system and

the MB events probe the effects of PMTs, tiles and ﬁbres, as

described in Sect. 3. Combining their information allows to

study the degradation of the optical system (tiles and ﬁbres)

of the TileCal, presented in Sects. 5.2 and 5.3.

5.1 Comparison of laser, caesium and minimum-bias

measurements

The effects of the LHC conditions on the cell response during

Run 2 are discussed in this section. Figure 16 shows the rel-

ative response variations of the A13 calorimeter cells to cae-

sium, MB events and laser pulses, respectively RCs,RMB

and RLas, as a function of time in Run 2. The assessment of

these quantities is detailed in Sects. 3.4,3.5 and 3.6. The cell

A13 is located in the EB, and due to the smaller amount of

upstream material, it is exposed to one of the highest radiation

doses as shown in Fig. 2.

The PMT gain, monitored with the laser system, is known

to decrease with increasing light exposure due to lower

secondary emissions from the dynode surfaces [40]. The

decrease in gain depends on several factors, including tem-

perature, intensity and duration of light exposure, as well

as the previous history of the PMT. When a PMT is ini-

tially exposed to light after a long ‘no light’ period, its gain

decreases rapidly and then slow stabilisation occurs [41].

This behaviour is demonstrated in Figs. 12 and 16. The data-

123

1313 Page 18 of 53 Eur. Phys. J. C (2024) 84:1313

taking in each year started after periods of inactivity. The

recovery periods, in which the PMT response to the laser

tends towards the initial conditions, coincide with the peri-

ods in which the LHC was not colliding protons. This is

consistent with the known behaviour of fatigued PMTs that

gradually return to their original operating condition after

the exposure is removed [42]. A global PMT gain increase of

0.9% per year is observed without any exposure (e.g. between

data-taking periods).

As already discussed, the responses to the caesium system

and MB events are sensitive to both the PMT gain changes

and the scintillator/ﬁbre degradation. The transparency of

these systems is reduced after radiation exposure [43]. In the

TileCal this is evident in the continued downward response

to caesium (or MB events) with increasing integrated lumi-

nosity of the collisions, despite the eventual slow recovery of

the PMTs as described above. In the absence of the radiation

source the annealing process is believed to slowly restore

the scintillator material, hence improving the collected light

yield. This can be seen in Figs. 9a, 10 and 16.Therate

and amount of scintillator damage and recovery are com-

plex combinations of factors, such as particle energies, tem-

peratures, exposure rates and duration, and are difﬁcult to

quantify.

To disentangle the effects of PMT and scintillator changes

one can study the laser compared to MB or caesium

responses. It can be seen in Fig. 16, that the responses

to caesium, laser and MB integrator systems show simi-

lar behaviour in 2015. This indicates no effect of scintil-

lator/ﬁbre irradiation. In 2016–2018, when the LHC deliv-

ered signiﬁcantly larger luminosity, the response to MB (and

caesium) events compared to the laser system diverges, indi-

cating the degradation of the scintillators and the ﬁbers. At

the end of Run 2, the maximum response loss in A13 is

approximately 16%, where around 8% is associated with

PMT response loss and the remaining 8% with scintillator

degradation.

5.2 Degradation of scintillator tiles and WLS ﬁbres

Scintillator tile and WLS ﬁbre ageing effects were deter-

mined during Run 2 using the signals produced by the 137Cs

source, MB collision events and the laser pulses. The rela-

tive light yield I/I0of the cells due to scintillator and ﬁbre

degradation is obtained by factoring out the PMT response

ﬂuctuations through the ratio between Cs/MB and Laser rel-

ative responses RCs/MB/Las as:

I/I0=RCs/MB

RLas .(8)

Figure 17 shows the relative light yield measured at the

end of Run 2 averaged over all TileCal barrel cells [44]. The

innermost layer A and the B11 and C10 cells have a light yield

loss between 4 and 10%. The uncertainty is around 1% and

the measurements are not yet sensitive to light yield degrada-

tion of B/BC and D cells. The observed light response degra-

dation is more important in the innermost cells, subjected to

larger particle ﬂuence and thus more ionising dose. The ion-

isation doses deposited in the different calorimeter cells are

obtained from simulation of pp collisions at a centre-of-mass

energy of √s=13 TeV using the Pythia 8 event generator

and Geant4 [45,46]. Figure 18 shows the simulated ionis-

ing dose corresponding to an integrated luminosity of 1 fb−1.

The largest doses, up to 20 Gy/fb−1, occur in the E cells. The

most irradiated cells in the calorimeter barrel are the A12 and

A13 cells, with doses of 0.5 and 0.4 Gy/fb−1, respectively.

To further investigate the effects of radiation exposure on

the performance of the TileCal active material, I/I0was

monitored during Run 2. The study allows the light yield

till the end of LHC operations and during the HL-LHC data-

taking period to be estimated. Figure 19 shows these mea-

surements as a function of the integrated delivered luminosity

and the average simulated dose deposited for A13 cells. The

Run 2 integrated dose is obtained by normalising the cell

averaged dose coefﬁcients presented in Fig. 18 to the mea-

sured integrated luminosity delivered by the LHC [8]. The

loss of light yield is quite smooth and increases with expo-

sure to radiation. In the study, the damage as a function of

dose dis modelled by an exponential function:

I/I0=p0e−d/p1(9)

The parameters p0and p1are obtained by minimising

the χ2function considering only the statistical uncertainty

in I/I0. The systematic uncertainties in the caesium, MB

and laser calibration systems (around 0.5%, 1.2% and 0.5%)

are propagated to the I/I0uncertainty. This uncertainty is

represented by the opaque band, which additionally covers

the spread of the dose within the large cell volume (around

50%). Based on the ﬁt result, the extrapolated light output

of the A13 cells is 75+9

−25% at the end of the LHC operation

(dose around 180 Gy).

While it is important to carefully monitor the performance

of the TileCal active material and make predictions well

ahead of time, doing such an extrapolation with current data

is affected by uncertainties not taken into account in this

study. For instance, no explicit recovery is incorporated in

the degradation model for long shutdown times. Further-

more, dose rate conditions may affect signiﬁcantly the degra-

dation rate. Measurements from the CMS-HCAL Collabo-

ration [49] conclude that smaller dose rates produce larger

degradation rates. In this scenario, the degradation rate, with

respect to dose, of TileCal cells would substantially decrease

in the HL-LHC phase given the increase of collision (dose)

rate. To estimate the decrease of the degradation effects, ﬁts

123

Eur. Phys. J. C (2024) 84:1313 Page 19 of 53 1313

Fig. 17 The measured relative

light yield I/I0(Eq. 8)ofthe

TileCal cells at the end of Run 2.

The uncertainty is of the order

of 1%

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1

1.2

1.3

1.4

1.6

ATLAS

Tile Calorimeter

Measured Relative Light Yield at the end of the Run 2

.94

.93

.92

A10

.99

.98

.99

.98

.99

.89

A12

.90

A13

.91

A14

.94

A15

.97

A16

.95

B11

.97

B12

.98

B13

.98

B14

.99

B15

.95

C10

.97

.98

00.25 0.50 0.75 1

Fig. 18 Simulated ionisation

dose deposited in the scintillator

tiles of the cells and in the

gap/crack scintillators in

4×4cm

2bins in r×z.The

study was performed using

50,000 inelastic pp collisions at

√s=13 TeV generated with

Pythia 8. The response of the

detector was obtained using the

simulation program Geant4.

The results are normalised to a

cross-section of σinel = 78.42 mb

and an integrated luminosity of

1fb

−1[45,46]

0 100 200 300 400 500 600

|z| [cm]

100

150

200

250

300

350

400

450

r [cm]

5−

4−

3−

2−

1−

]

-1

Total ionising dose [Gy/fb

ATLAS Simulation = 13 TeVsGEANT4,

1.9e-1A2

2.0e-1A3

2.0e-1A4

2.1e-1A5

2.1e-1 A6

2.2e-1 A7

2.2e-1 A8

2.3e-1 A9

2.6e-1 A10

2.7e-1

2.9e-2 B2

3.1e-2 B3

3.2e-2 B4

3.2e-2 B5

3.3e-2 B6

3.5e-2 B7

3.6e-2 B8

4.1e-2 B9

7.4e-2

4.0e-3 C2

4.3e-3 C3

4.4e-3 C4

4.4e-3 C5

4.5e-3 C6

4.7e-3 C7

6.2e-3 C8

2.7e-2

7.0e-4 D1

7.6e-4 D2

7.8e-4 D3

7.0e-3

A12

5.3e-1 A13

4.1e-1 A14

2.6e-1 A15

1.0e-1 A16

3.2e-2

B11

1.1e-1 B12

4.8e-2 B13

2.6e-2 B14

1.0e-2 B15

6.2e-3

9.2e-3 D6

1.5e-3

C10

1.3e-1

5.4e-2

3.6e-1

7.6e-1

2.3e+0

9.3e+0

of the function in Eq. (9) are performed to the relative light

yield determinations as a function of the simulated dose for

the TileCal cells exhibiting the largest degradation, i.e. those

in the A layer, B11 and C10, see Fig. 17. The obtained ﬁt-

ted parameters, driving the degradation rate, p1, are repre-

sented as a function of the average dose rates of the cells in

Fig. 20. The average dose rates correspond to the cell aver-

aged dose coefﬁcients presented in Fig. 18 multiplied by the

LHC average instantaneous luminosity in Run 2. The results

obtained by CMS for the ﬁrst and seventh HCAL layer fea-

turing Polystyrene-based scintillators from Kuraray (SCSN-

81) [49] are also shown in the ﬁgure and, despite differences

in calorimeter arrangement and scintillator material (Tile-

Cal uses Polystyrene+(1.5%)pTP+(0.04%)POPOP [5]), the

agreement with TileCal is good. The TileCal data are ﬁtted

with a power law function to model the dose rate depen-

dency of the cell ageing allowing an extrapolation to the

HL-LHC that takes into account an expected dose rate being

seven times larger (dashed vertical line). The obtained p1

value for the A13 cells in the HL-LHC phase is 1724 Gy.

This extrapolation is also drawn in Fig. 19 (solid curve).

According to this study, one expects for the A13 cells a nor-

malised light response of 35+17

−27% at the end of the HL-LHC

runs, approximately two times larger than the value obtained

without taking into account the different dose rate expected at

HL-LHC (dashed curve). In the ﬁgure, the extrapolated curve

can also be compared with the results of laboratory irradi-

ations [47] where the dose rates were enormously higher

and the scintillators were measured one month after irradi-

ations, having recovered most of the suffered damage. Also

the results obtained by analysing the MBTS data discussed in

Sect. 5.3 highlight the reduction of the response degradation

by increasing the dose rate.

123

1313 Page 20 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 19 Average relative light yield (I/I0) measurements based on the

caesium system (dots) and integrated minimum-bias currents (trian-

gles) for A13 cells as a function of average simulated dose dand LHC

integrated luminosity. The dashed curve corresponds to the ﬁt to the

function in Eq. (9) to the data. The surrounding opaque band represents

the total uncertainty in the ﬁt including the RMS of the dose distribution

within the cell and systematic uncertainties in I/I0due to the intrinsic

precision of the caesium, MB and laser measurements. The solid curve

represents the expected average I/I0of the A13 cells in the HL-LHC

phase including dose rate effects (see the text). The surrounding semi-

transparent band is the total uncertainty on this extrapolation, obtained

by propagating the uncertainty sources of the study. Results from mea-

surements of bare scintillators performed one month after irradiations

with much higher dose rate made in the laboratory before the detector

construction are also shown [47]. An exponential function is ﬁtted to

the data obtained from irradiations with γs (open squares) and hadrons

(full squares). Dashed vertical lines represent the expected dose by the

end of the LHC (450 fb−1) and HL-LHC (4000 fb−1)[48]

7−

10 6−

10 5−

10 4−

[Gy/s]d

Dose Rate

200

400

600

800

1000

1200

1400

1600

1800

[Gy]

ATLAS

Tile Calorimeter

Run 2

A1 A2 A3 A4

A5 A6 A7 A8

A9 A10 A12 A13

A14 A15 A16 B11

C10

10×3.1

CMS HE Cal

Fig. 20 The degradation rate parameter p1obtained from the simple

exponential model (I/I0=p0e−d/p1) as a function of the average sim-

ulated dose rate ˙

dfor the most exposed cells. Results from a similar

study performed using the CMS Hadron Endcap Calorimeter measure-

ments are also displayed (crosses) [49]. The vertical error bars on the

TileCal data points represent the total uncertainty including the I/I0

measurement uncertainty and the dose spread within the cell volume.

The nominal points are ﬁtted with a power law function (continuous

curve). This function is extrapolated to the higher dose rate region

(dashed curve) expected at the HL-LHC phase and populated by the

CMS data. The dashed vertical line indicates the expected dose rate of

the A13 cells in the HL-LHC

5.3 Degradation of the MBTS system

The counters of the MBTS system are read out using the same

electronics as the TileCal. Due to high irradiation dose, scin-

tillator, ﬁbre and PMT performances degraded throughout

Run 2 and large MBTS signal reductions were observed. The

average total ionising doses, estimated by using the method

described in Sect. 5.2,are0.62×103Gy/fb−1for inner coun-

ters and 0.83 ×102Gy/fb−1for outer counters [45,46]. The

degradation of the MBTS system is determined using laser

and MB data collected during pp collision runs. The PMTs

were operated at 500 V. The MBTS response is obtained by

normalising the measured MB currents to the ATLAS instan-

taneous luminosity [8].

Figure 21a shows the response variation of the inner and

outer counters relative to the ﬁrst run of Run 2, as a func-

tion of time. The relative variations of the PMT responses as

measured by the laser system are also shown.

At the beginning of Run 2, the MB PMT currents were at

the level of 15 and 10 nA/1030s−1cm−2for the inner and outer

counters respectively. By end of 2015 (3.2 fb−1of integrated

luminosity), the inner (outer) counters have lost almost 55%

(35%) of their response. This difference is due to the larger

irradiation density of the inner counters, being closer to the

beam line. At the beginning of each new year’s data-taking

period, a relative recovery of the response of about 30% is

observed. This is due to the technical stop periods where the

scintillators and ﬁbres are not irradiated further and partially

recover some of their efﬁciency.

Laser data allowed the PMT variations over the Run 2

period to be evaluated. The inner and outer counters show a

rapid decrease of the average PMT response by about 20% at

the beginning of Run 2. This degradation is expected as the

PMTs are receiving high light output from the scintillators

at that time, inducing a large anode current and a signiﬁcant

amount of charge being integrated. After this rapid decrease,

the responses of the PMTs become more stable and a general

up-drift can be noticed. This behaviour is understood by the

rapid degradation of the MBTS scintillators/ﬁbres that results

in much less light received by the PMTs. Consequently the

integrated PMT anode currents decrease causing the down-

drift to cease and eventually to reverse its course.

To measure the light yield degradation of the MBTS scin-

tillators and ﬁbres, PMT variations are factored out from MB

data as expressed in Eq. (8). The results as a function of the

total ionising dose are shown in Fig. 21b. The decline appears

to follow an exponential decay curve up to 15 kGy with sim-

ilar degradation rate for inner and outer counters. With the

increase of the doses, the inner counters continue to decrease

exponentially with a slowing rate caused presumably by sat-

uration effects in the scintillating material.

123

Eur. Phys. J. C (2024) 84:1313 Page 21 of 53 1313

100

120

140

160

]

-1

Integrated Delivered Luminosity [fb

07/15 01/16 08/16 02/17 09/17 04/18 10/18

Date [mm/yy]

100−

80−

60−

40−

20−

Average Response Variation [%]

MBTS Inner

MBTS Outer

PMT Inner

PMT Outer

= 13 TeVsRun 2,

ATLAS

Tile Calorimeter

(a)

0 1938577695

Dose d [kGy]

2−

1−

I/I

Inner Scintillators

Outer Scintillators

-d/26

+ 0.14 e

-d/3.0

0.86 e

= 13 TeVsRun 2,

ATLAS

Tile Calorimeter

(b)

Fig. 21 a The average response variation of the MBTS inner (down

triangles) and outer (up triangles) counters as a function of time during

Run 2. The circle (diamond) markers show the relative response of the

PMTs of the inner (outer) counters. The LHC delivered luminosity is

shown by the shaded area. bThe average relative light yield (I/I0)of

scintillators and ﬁbres of inner (down triangles) and outer (up triangles)

counters as a function of total ionising dose during Run 2. The values

are the averages of the corresponding determinations obtained for the

inner and outer counters. The uncertainties correspond to the RMS of

the counter response distributions. The function obtained by ﬁtting the

inner MBTS data points is also shown (solid curve)

6 Data quality analysis and operation

A collection of tools continuously monitors the detector hard-

ware and data acquisition systems during their operation.

Some are fully automated to effectively address problems

in real time (online) to protect hardware and scrutinise the

quality of the output data. In the following, the Detector Con-

trol System (DCS), Sect. 6.1, and the use of calibration runs

to check for faulty hardware components, Sect. 6.2,aredis-

cussed. Online data quality assessment and monitoring are

presented in Sect. 6.3. After data are recorded, ofﬂine moni-

toring tools provide key information for analysers to improve

data quality and to address more challenging data corruption

issues. For cases of (semi-) permanent problems, data quality

ﬂags are assigned to portions of the affected data, indicat-

ing whether those data are usable for physics analyses with

care (on an analysis-dependent basis) or must be discarded

entirely.

6.1 Detector control system

The ATLAS detector control system (DCS) ensures the

coherent and safe operation of the whole experiment [50].

The TileCal DCS [51] is part of the ATLAS DCS being

responsible for the control of the low- and high-voltage

systems and the detector’s infrastructure. The DCS exe-

cutes actions initialised by the operator or automatic actions

based on conditions for the monitored data, such as auto-

matic recovery of power supplies from an abnormal shut-

down or shutdown electronics due to high temperature or

over-current. Alarms and other notiﬁcations (email, SMS)

are triggered to alert the experts about any abnormality.

The relevant monitored data and all alarms are stored in

databases, allowing easy access to the data. For example,

the DCS is able to adjust the various high-voltage levels for

each of the TileCal’s PMTs, in the range of [HVinput +1V,

HVinput +360 V][52], while monitoring it with 0.1 V accu-

racy. Keeping a stable applied voltage is the main goal of the

high-voltage system since the gain of the PMT is a function

of the applied voltage, HV:

G=α×HVβ(10)

where αand βare characteristic parameters of each PMT. In

the TileCal LB and EB, the average HV is 640 V and 700 V,

respectively, while the average βis 7.07 and 6.95. Therefore,

a variation in voltage of 1 V causes a gain variation of 1.1%

(1%) in the LB (EB).

The archived DCS data allow the stability of the HV

applied to the TileCal’s PMTs to be studied. The analysis

of the data collected in 2015 and 2016 indicated very few

problematic channels. Only 0.2% of the PMTs operated with

an HV instability larger than 0.5 V [52]. Consistent results

were obtained by analysing data collected over the entire

Run 2. A portion of the faulty channels may be due to mon-

itoring problems. However, hardware failures are the most

common cause.

The ATLAS DCS is based on a supervisory control and

data acquisition (SCADA) [53] commercial software. The

DCS uses a set of guidelines and software framework com-

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1313 Page 22 of 53 Eur. Phys. J. C (2024) 84:1313

Fig. 22 The hierarchy of the

TileCal DCS within the ATLAS

DCS [51]

ponents that allow an easy and coherent integration of the

ATLAS detectors as a single control system. The TileCal

DCS is distributed among two of the three functional lev-

els of the DCS back-end (BE) hierarchy, as can be seen in

Fig. 22. It is distributed among six SCADA systems. Four

of them are identical and independent in functionality, each

being responsible for the low and high-voltage systems of one

TileCal partition. The infrastructure supervises the detector’s

common components (detector cooling, calibration systems,

etc) while the sub-detector control station acts as the top

layer of the TileCal hierarchy combining all the SCADA

systems. A ﬁnite state machine (FSM) toolkit [54]isused

to model the DCS BE hierarchy. This allows for full con-

trol of the detector and, at the same time, visualisation of

either the global detector state and the status at the top level

or the individual voltages at the lower level. Due to its sim-

plicity of usage, the FSM is the most commonly used tool by

detector experts and users. In preparation for Run 2, the DCS

went through important updates and upgrades [51]. The main

improvement was the migration of the non-SCADA software

to a fully Linux based system. For example, the OLE (object

linking and embedding) for process control data access was

migrated to Open Platform Communications Uniﬁed Archi-

tecture. Apart from the changes required by software migra-

tion, it was also necessary to replace control blocks due to

the replacement of hardware (a new 200 V DC system and

new laser calibration system).

6.2 Calibration run validation

The analysis of calibration runs mainly allows checks for

faulty hardware components. During the long shutdowns,

the faulty components are included in the repair list of the

maintenance team. The following types of calibration runs

are monitored:

•Laser calibration runs. There are two types of laser runs:

‘Laser LG’ and ‘Laser HG’, which use two different

intensities of the laser signal.

•Charge injection runs. Three types of runs are used: ‘CIS’

calibration runs where a scan with the variable injected

charge and phase is performed, and ‘MonoCIS LG’ and

‘Mono-CIS HG’ runs with a constant value of injected

charge.

•Pedestal runs with no beams circulating in the LHC and

thus with no signal in the detector.

Raw data from calibration runs are reconstructed ofﬂine

using the dedicated software based on the ATLAS data pro-

cessing framework Athena [10]. It produces plots and his-

tograms that undergo futher tests to identify potential prob-

lems. Two types of tests are employed. The ﬁrst checks the

consistency of data (e.g. parity bits, data header, etc) sent

by individual DMU chips. The second type of tests moni-

tors the quality of the signal read from individual channels

(e.g. digital errors, RMS of signal amplitude, timing shifts,

etc). The ﬁrst type of tests provides a result for each DMU

on the module while the second type provides a result for

each channel. Some tests are run for all types of calibration

runs while other tests are run-type speciﬁc. Each test takes

some speciﬁc value obtained for each channel or DMU and

compares it to a predeﬁned threshold or a range of values.

Based on this comparison it assigns a status (called data qual-

ity monitor or DQM status) to each channel (or DMU). The

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Eur. Phys. J. C (2024) 84:1313 Page 23 of 53 1313

status is Green if the expected value is observed. If a problem

is found, the status can be Yellow or Red depending on its

severity. Statuses of individual channels or DMUs are then

propagated into the DQM module test status:

•Green status: all channels/DMUs test results are within

expected range.

•Yellow status: some problems, usually no more than one

channel affected.

•Orange status: more serious problems, usually two or

more channels affected.

•Red status: serious problems.

•Undeﬁned status: there is not enough data to evaluate the

test.

The module’s overall DQM status is then treated as the

worst status of all module tests.

6.3 Online data quality assessment and monitoring

Data quality monitoring begins in real time in the ATLAS

control room. Online shifters on duty serve as a ﬁrst line

of defence to identify serious detector-related issues. During

periods of physics collisions, the TileCal has experts in the

ATLAS control room 24 h per day and a handful of remote

experts available on call to assist in advanced interventions.

The primary goal is to quickly identify and possibly correct

any problem that cannot be ﬁxed later in software, and that

can result in overall data loss.

The ATLAS data quality framework is designed to per-

form automatic checks of the data and to alert experts to

potential problems that warrant further investigation [55].

This framework allows data quality monitoring at various lev-

els of the ATLAS data ﬂow. The subset of data reconstructed

online is quickly made available to the online shifters via

display tools. The automatic tests performed on the recon-

struction output include compatibility checks between the

observed distributions from the monitoring data and refer-

ence histograms taken from a past run that is both free of

data quality issues and taken with similar machine operating

conditions. Other tests might involve checks on the number

of bins in a histogram above a predeﬁned threshold, or checks

on the gradient of a distribution. For example, histograms that

monitor readout errors should always be empty under ‘ideal’

conditions. If a bin in such a histogram has a non-zero num-

ber of entries, a ﬂag would be raised to alert the shifter to

the problem. Online event reconstruction also allows con-

trol room experts to monitor reconstructed physics objects,

such as jets or missing transverse momentum, permitting

real-time monitoring of combined performance in addition

to detector status. The data quality monitoring framework

takes the results of tests on individual histograms and prop-

agates them upwards through a tree, resulting in a set of

top-level status ﬂags, which can be viewed on the data qual-

ity monitoring display. Monitoring histograms are updated to

include additional data every few minutes as newly available

data are reconstructed. In this way, online monitoring allows

hardware- or software-related issues to be caught in real time

and rectiﬁed to minimise their impact on collected data.

In addition to the global ATLAS data quality monitoring,

TileCal speciﬁc data are reconstructed and validated. This

allows more events to be reconstructed and more detailed

monitoring histograms to be produced. Online reconstruction

is done in the same way as it is done ofﬂine (see Sect. 6.4).

TileCal speciﬁc automatic checks are focused mainly on

monitoring histograms that accumulate entries per luminos-

ity block (one luminosity block nominally spans one minute).

These allow detection of problems and to address them within

that period during the run. For example, if a test detects that

the fraction of corrupted data in a single TileCal module

goes above a certain threshold, a request to power-cycle the

corresponding module is issued. Then the DCS decides if a

power-cycle should be performed immediately or be ignored

because the module is in the exclusion list or was recently

power-cycled. During physics runs, in addition to collisions

data, the TileCal also collects special laser data when every

TileCal PMT receives laser light. These laser data are recon-

structed online to monitor timing and address timing jumps,

as described in Sect. 3.1.2.

Common problems identiﬁed by TileCal experts during

the online shifts include hardware failures that do not auto-

matically recover, or software conﬁguration problems that

might present themselves as data corruption ﬂags from the

ROD data integrity checks. The trigger efﬁciency and data

acquisition, as well as higher-level reconstruction data qual-

ity, might be inﬂuenced by such problems.

6.4 Ofﬂine data quality review

Once the data are recorded, a two-stage ofﬂine DQ assess-

ment ensues [7]. Soon after the data are taken, a small fraction

is quickly reconstructed using the ATLAS Athena software

framework [10]. Reconstructed data are then used by the

ofﬂine data quality experts with more complex tools to eval-

uate the quality of the data. The experts are given 48 h to

identify, and, where possible, to correct problems, before the

bulk reconstruction of the entire run is made. The TileCal

ofﬂine experts can update the conditions database, where

information such as the calibration constants and status of

each channel is stored. Channels that suffer from high levels

of noise have calibration constants in the database updated

accordingly. For channels that suffer from intermittent data

corruption problems, data quality ﬂags are assigned to the

affected data to exclude the channels in the full reconstruc-

tion during the problematic period. This 48-h period is also

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1313 Page 24 of 53 Eur. Phys. J. C (2024) 84:1313

used to identify cases of digitiser timing jumps and to add the

additional time phases to the time constants of the digitiser

affected to account for the magnitude of the time jump.

Luminosity blocks can be ﬂagged as defective to iden-

tify periods of time when the TileCal is not operating in its

nominal conﬁguration. These defects can either be tolera-

ble whereby corrections are applied but additional caution

should be taken while analysing these data, or intolerable in

which case the data are not deemed suitable for physics anal-

yses. Defects are entered into the ATLAS data quality defect

database [56] with the information propagating to analyses

and to integrated luminosity calculations. Removing all data

within that time can accumulate to a signiﬁcant data loss. For

rare situations where only a few events are affected by the

data corruption, an additional error-state ﬂag is introduced

into the reconstruction data. This ﬂag is used to remove

such events from the analysis. Once all ofﬂine teams per-

form the review, the entire run is reconstructed using the

most up-to-date conditions database. Subsequently the data

can be re-reconstructed when reconstruction algorithms are

improved and the conditions database is further reﬁned to

improve the description of the detector. These data repro-

cessing campaigns typically occur several months after the

data are taken.

6.5 Summary of TileCal operations in Run 2

The TileCal operated very smoothly during Run 2. In 2015,

there were no data lost due to DQ issues in the TileCal. In

2016 most of the inefﬁciency attributed to the TileCal was

due to data lost in two cases when four consecutive modules

were disabled due to a failure in the ROD-ROS links. These

events induced a loss of 61 pb−1and 42 pb−1(0.29% of the

total integrated luminosity of 35.5 fb−1in 2016). Most of

the data loss from the TileCal during 2017 corresponded to a

single run when four consecutive modules were disabled for

8 h due to an auxiliary board [3] issue. During the attempt

to ﬁx the auxiliary board the entire LBA partition went off

for 2 h. This event induced a loss of 265 pb−1(0.57% of the

total integrated luminosity of 46.4 fb−1in 2017). In 2018,

there was no data lost due to DQ issues in the TileCal. The

DQ efﬁciency for each year of Run 2 and for the entire Run

2 are summarised in Table 2.

Failure of some components, like cooling or an interface

card, causes the exclusion of all channels (thus all cells) of

a module from the reconstruction. Failure of other compo-

nents affects only a part of a module. For example, a failure

of a HV card renders every second channel of a half-module

non-operational, while keeping all readout cells operational

thanks to the readout redundancy. The next example is a fail-

ure of a digitiser [3]. In this case, six corresponding channels

and two or three cells are excluded. Figure 23 shows the

fraction of channels and cells removed (masked) as a func-

tion of time in Run 1 and Run 2. The shaded regions cor-

respond to maintenance periods. The most common issues,

which are addressed and repaired during this time, corre-

spond to cooling, HV, front-end electronics or the trigger.

Regular maintenance helped to keep the fraction of inefﬁ-

cient cells below 1.1%. Only a residual number of channels

remain inoperational after long maintenance periods. These

correspond to cases where there was lack of time for the full

repair, or where accessibility conditions and risk manage-

ment prevented intervention.

7 Performance studies

The studies reported in this section allow the performance

of the reconstruction and calibration methods described in

the previous sections to be veriﬁed. The results obtained by

analysing isolated muons, single hadrons and jets are dis-

cussed. The cell noise levels obtained by analysing experi-

mental and simulated data are compared. The performance

of the minimum-bias trigger system and the improvements

made to the ATLAS muon trigger system by implementing

the information provided by the TileCal are also presented.

7.1 Energy response to single isolated muons

The interaction of muons with matter is a well-understood

process [57,58], and events with muons allow comparison

of the equalisation of the cell response, its stability and the

EM scale setting. In this section, results of the studies of

the calorimeter response to muons produced by W→μν

decays from pp collisions are reported. These muons have

momenta in the range 20 to 80 GeV and lose energy in the

TileCal primarily due to ionisation. The energy loss per unit

path is close to the minimum ionising level for the momenta

considered and hence small compared to the muon energy

over the full path traversed in the calorimeter. This makes

the muons from W→μν ideal test particles to scan the

entire TileCal volume.

In the analysis, the muon tracks measured by the pixel and

SCT detectors are extrapolated through the calorimeter vol-

ume taking into account detector material and the magnetic

ﬁeld [59]. To compute the path, x, travelled by the muon

in a cell, a linear interpolation is performed to determine the

entry and exit points of the muon in the crossed cell. The path

and the energy deposited in the cell, E, are used to com-

pute the muon energy loss per unit distance, E/x.The

distributions of this quantity can be described by a Landau

function convoluted with a Gaussian distribution, where the

Landau part describes the actual energy loss and the Gaus-

sian part accounts for resolution effects. However, due to

long tails from rare energy loss mechanisms, such as ener-

getic delta electrons and bremsstrahlung, the ﬁts show small

123

Eur. Phys. J. C (2024) 84:1313 Page 25 of 53 1313

Tabl e 2 The TileCal DQ efﬁciency during stable-beams pp collision physics runs at √s=13 TeV for each year of Run 2 and for the entire Run 2.

For completeness the corresponding ATLAS efﬁciencies are reported [7]

2015 2016 2017 2018 Entire Run2

TileCal DQ efﬁciency [%] 100.00 99.31 99.41 100.00 99.65

ATLAS DQ efﬁciency [%] 88.79 93.07 95.67 97.46 95.60

Fig. 23 The fraction of

channels and cells removed from

the reconstruction (masked) as a

function of time during Run 1

and Run 2. The number of

masked cells (channels) at the

end of Run 2, 3 December 2018,

is about 0.5% (1%). The hatched

area represents the maintenance

periods of the detector

Date [Month/year]

Dec/10 Dec/11 Dec/12 Dec/13 Dec/14 Dec/15 Dec/16 Dec/17 Dec/18

Masked Channels and Cells [%]

Masked Channels

Masked Cells

Maintenance

ATLAS

Tile Calorimeter

χ2probability values. For this reason the truncated mean

E/xF=1%, calculated without considering 1% of the

events in the high tail of the distributions, is used to deﬁne the

muon response. Typical values are about 1.5 MeV/mm. The

truncated mean shows slight non-linear behaviour with x.

This effect and other non-uniformities, such as pile-up, the

differences in momentum and incident angle spectra are, to a

large extent, reproduced by the MC simulation. To compen-

sate for these effects, the ratio of the E/xF=1% obtained

using experimental and simulated data:

R≡E/xdata

F=1%

E/xMC

F=1%

(11)

is then used in the analysis. Differences between the Rvalues

obtained for different cells may indicate cell miscalibration.

Deviations of the values from unity may indicate an inaccu-

rate setting of the EM energy scale.

7.1.1 Selection of isolated muons

The results presented here were obtained by analysing

proton–proton collision data collected in 2015–2016, 2017

and 2018. The 2015 and 2016 samples were merged together

due to low numbers of events in each of them. Events were

collected using the L1 muon trigger that accepts events with

muons originating from the interaction point [60]. Three

further kinematic cuts are used to select W→μν events

and to suppress background from multijet processes: num-

ber of muon tracks Nμ=1, transverse mass540 GeV ≤

mT≤140 GeV and missing transverse momentum [61]

30 GeV ≤Emiss

T≤120 GeV. The muons with momentum

20 GeV ≤p≤80 GeV are retained. The lower limit reduces

multiple scattering effects while the upper limit ensures that

muons lose their energy primarily via ionisation. The con-

tribution from nearby particles in the energy deposited in a

cell is suppressed by only considering well isolated muon

tracks [6]. The selection requirements x>100 mm and

E>60 MeV are applied to reduce noise contributions. The

cells with |η|<0.1 are not considered in the analysis since

in the TileCal the scintillator tiles are oriented radially and

the energy deposited by muons with a trajectory close to this

direction is measured with poor accuracy due to large vari-

ations of sampling fraction as function of the impact point.

The number of retained tracks is about 300 million.

The MC simulation uses the Geant4 toolkit that provides

the physics models of particle interactions with material,

the ATLAS geometry description and the tracking tools as

described in Sect. 3.8. The simulated W→μν events were

generated using Sherpa [62] at next-to-leading order inter-

faced with Pythia 8 [63] for the parton showering. The

number of simulated events is approximately 200 million.

The energy deposited in the cells was digitized and recon-

5ThetransversemassmTof a single heavy particle in a semi-

invisible decay into particles 1 and 2 is deﬁned by mT=

2pT,1pT,2(1−cosφ12)in the approximation of massless decay prod-

ucts (m1=m2=0), with pT,1(2)denoting the transverse momentum

magnitude of the particle 1 (2) and φ12 the azimuthal distance between

the decay products [58].

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1313 Page 26 of 53 Eur. Phys. J. C (2024) 84:1313

structed using the same procedure as applied in the case of

experimental data. The retained events pass the same crite-

ria used in the selection of experimental data. The relevant

kinematic distributions show good agreement between data

and MC with observed discrepancies only in bins whose con-

tents are a negligible proportion of the total yield. Using MC

events, the purity of the selected track sample is estimated to

be 98%.

7.1.2 Cell response uniformity

As shown in Fig. 24, in the calorimeter one can distinguish

74 rings of cells, 46 in the LB and 28 in EB, each consisting

of Nr=64 cells with the same values of the coordinates r

and zand different value of φ. Due to the symmetry of the

calorimeter and of the particles produced in pp collisions,

one expects that the ratios in Eq. (11) computed using muons

crossing any cell cof the same ring r,Rr,c, are equal.

The estimated value for the ratio deﬁned by Eq. (11)for

a given ring, Rr, is determined by maximising the Gaussian

likelihood function:



c=1

√2πσ2

r,c+s2

exp −1

2Rr,c−Rr2

σ2

r,c+s2

r(12)

where σr,cis the statistical uncertainty in the determination of

Rr,c. The other parameter determined in the likelihood func-

tion maximisation, sr, describes the response non-uniformity

of the cells due to differences in the optical and electronic

read-out systems not described in the simulation and poten-

tial mis-calibration of cells of the ring. The parameter sr

determined from the ﬁt is 2.4%. The cell non-uniformity was

previously measured in ATLAS using cosmic rays [6] and at

test beams [11] using electrons incident at the centre of A

cells with an angle of 20◦. Similar results are obtained. The

analysis shows no problematic cells with a value of Rr,cthat

deviates signiﬁcantly from Rr.

Figure 24 shows the estimates of Rrobtained for the dif-

ferent rings by analysing 2017 data. For a given radial layer,

the values are uniform within about 1%. Similar results are

found by analysing the 2015–2016 and 2018 data.

7.1.3 Radial layer calibration

The estimated response Rlfor the six radial layers l: LB-A,

LB-BC, LB-D, EB-A, EB-B, and EB-D are obtained from

the truncated mean ratios (Eq. (11)) of the experimental and

simulated E/xdistributions of all the cells belonging

to the layer. The results obtained by analysing the data col-

lected in the three periods are reported in Table 3. The sta-

tistical and systematic uncertainties are shown separately. To

estimate systematic uncertainties, the selection parameters

were varied around their nominal values (see Sect. 7.1.1).

The systematic uncertainties dominate. In each year the stan-

dard deviation of the distributions of the measurements listed

in the Table is 1.5%. The maximum deviation of the values

of Rlfrom 1 is 0.029.

7.1.4 Time stability

The determination of the stability of the Rrvalues discussed

in Sect. 7.1.2 allows quantiﬁcation of how well the calibration

compensates for time-dependent variations of the calorimeter

response. For the cells of each ring r, a determination is

obtained by maximising the likelihood function:

L=exp −1

2(Rr−ˆ

Rr)T[Cr]−1(Rr−ˆ

Rr)(13)

The components of the vector Rr=(R1

r,R2

r,R3

r)arethe

responses of the cells of a ring robtained by maximising the

likelihood function (Eq. (12)) using data collected in 2015–

2016 (R1

t), 2017 (R2

t) and 2018 (R3

t) respectively. The com-

ponents of the vector ˆ

Rr=(ˆ

r,ˆ

r) are the correspond-

ing parameter estimates. The covariance matrix Crtakes into

account correlations between the determinations performed

in the different periods.

Figures 25a, b show the relative variations of the ring

responses between 2015–2016 and 2017:

r(2015–2016 →2017)≡ˆ

Rr(2017)−ˆ

Rr(2015–2016)

(14)

and between 2017 and 2018:

r(2017 →2018)≡ˆ

Rr(2018)−ˆ

Rr(2017)

Rr(2017)(15)

respectively. The uncertainties are obtained by combining

statistical and systematic effects in quadrature and taking

into account correlation effects. In both cases the most prob-

able value is +0.5%. The 95% conﬁdence intervals of the

variations of the cell response between 2015–2016 and 2017

and between 2017 and 2018 are found to be [−1.3%,3.3%]

and [−2.4%,+2.5%]respectively.

7.2 Energy response to single isolated hadrons

The calorimeter energy response is probed by measuring the

energy deposited in the calorimeter by isolated hadrons pro-

duced in the pp collisions. The determination is obtained

using the ratio:

R=E/p(16)

123

Eur. Phys. J. C (2024) 84:1313 Page 27 of 53 1313

1.025 1.016 1.0181.0191.0161.027

0.989 0.997 0.987 1.007 1.011 1.008 1.017 0.993 1.0120.9691.0000.9931.0020.9951.0011.0110.9781.012

1.007 0.993 0.991 0.985 0.992 0.992 0.993

1.001

0.9830.9920.9950.9930.9940.9970.990

0.979

1.063 1.039 1.010 0.996 1.030

1.003 0.975 0.987 1.006 0.988

1.075

1.053

589.0689.0

1.0651.0361.0161.0041.000

1.0131.0020.9910.9770.993

1.054

1.061

1.0090.993

0.8

0.85

0.9

0.95

1.05

1.1

1.15

1.2

= 0.0η 0.1-0.1 0.2 3.0 2.0--0.3 0.4-0.4 0.5-0.5 0.6-0.6 0.7-0.7 0.8-0.8 0.9-0.9 1.0-1.0 1.1-1.1 1.2-1.2

1.3-1.3

1.4-1.4

1.5-1.5

1.6-1.6

ATLAS

Tile Calorimeter

A sideC side

D0 D1 D2 D3D1D2D3

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A1A2A3A4A5A6A7A8A9A10

BC1 BC2 BC3 BC4 BC5 BC6 BC7 BC8

BC1BC2BC3BC4BC5BC6BC7BC8

A12 A13 A14 A15 A16

B11 B12 B13 B14 B15

C10

D5 D6

A12A13A14A15A16

B11B12B13B14B15

C10

D5D6

Fig. 24 Visualisation of the TileCal in the (z,r) plane showing the 2017 estimated values of the ratios Rrof the cells of a given ring r(see the

text). The values are obtained by maximising the likelihood function in Eq. (12). The statistical uncertainty in each determination is about 0.5%

Tabl e 3 The response ratio Rlof the cells of different radial layers in the LB and EB obtained by analysing 2015–2016, 2017 and 2018 data.

Statistical (ﬁrst value) and systematic (second value) uncertainties are shown

Layer Rl(2015–2016) Rl(2017) Rl(2018)

LB-A 0.988 ±0.001 ±0.003 0.996 ±0.002 ±0.007 0.996 ±0.001 ±0.004

LB-BC 0.984 ±0.001 ±0.001 0.993 ±0.001 ±0.002 0.992 ±0.001 ±0.003

LB-D 1.014 ±0.001 ±0.004 1.019 ±0.001 ±0.003 1.024 ±0.001 ±0.002

EB-A 1.006 ±0.003 ±0.006 1.029 ±0.002 ±0.006 1.016 ±0.002 ±0.006

EB-B 0.978 ±0.002 ±0.002 0.990 ±0.001 ±0.006 0.989 ±0.001 ±0.004

EB-D 0.982 ±0.001 ±0.004 0.990 ±0.001 ±0.007 0.997 ±0.001 ±0.004

where Eis the energy measured by the calorimeters and p

the momentum measured by the inner detector. The particles

have a momentum below 20 GeV and the precision of the

measurement is dominated by the energy resolution [1]. The

events analysed were produced in pp interactions in 2017 at

a centre-of-mass energy √s=13 TeV with an average of

the mean number of interactions per bunch crossing within

a luminosity block, μ, of about 2.

At the trigger level, the events were selected requiring at

least one counter with a signal above the threshold value in

each side of the MBTS system (see Sect. 4.3). Each event is

required to have a well reconstructed vertex with at least four

well reconstructed associated tracks with pT>400 MeV.

Each track selected for this study is required to have pT>

500 MeV and |η|<2.5. The reconstruction of the momen-

tum pis described in [1]. To select isolated single charged

hadrons, no other track is allowed within a cone of R<0.4

centred on the considered track. The energy measurement

Eis obtained using ensembles of cells (topological clus-

ters) [64]. A cluster of cells iis associated with the track k

if:

R=(η(trackkj,clusteri))2+(φ (trackkj,clusteri))2

<0.2(17)

where φ(trackkj,clusteri)is the smallest angle in the trans-

verse plane between track kand cluster i.Theη(trackkj)and

φ(trackkj)are the values of the extrapolated track in the layer

jof the cluster with the largest deposited energy. The energy

of a track corresponds to the sum of the energy deposited in

all the cells of the clusters associated with the track [65]. The

cell energies are reconstructed and calibrated at EM scale

using the procedures described in Sects. 2and 3.

Additional requirements are applied to ensure that tracks

reach and deposit their energy in the TileCal layers, and to

reduce the contamination from neutral particles and muons.

These are:

•Momentum of the track, ptrack, larger than 2 GeV and

|ηtrack|<1.7.

•The energy deposited by the hadron in the LAr EM

calorimeter [66], ELAr <1 GeV, compatible with that

of a minimum ionising particle (MIP), so that the hadron

deposits almost all its energy in the TileCal.

•The energy deposited in TileCal is required to be at least

70% of the total energy deposited in the calorimeters,

ETile/(ELAr +ETile)>0.7.

The last two criteria effectively reduce the contribution

from neutral particles decaying electromagnetically since

123

1313 Page 28 of 53 Eur. Phys. J. C (2024) 84:1313

0.008 0.004 0.0050.007-0.0020.010

-0.013-0.0030.002 0.007 0.025 0.006 0.024 0.025 0.008-0.0050.0120.0030.0040.0130.0100.0050.0060.003

0.029 0.013 0.008 -0.002 0.002 -0.006 0.005

0.002

0.0120.0090.0080.0200.0210.0160.005

0.011

0.024 0.021 0.021 -0.001 0.002

0.021 0.006 0.004 0.031 0.007

0.014

0.015

700.0700.0

0.0300.0220.0310.0400.020

-0.0100.0150.0160.0020.022

-0.020

-0.023

0.0040.008

0.08−

0.06−

0.04−

0.02−

0.02

0.04

0.06

0.08

= 0.0η 0.1-0.1 0.2 3.0 2.0--0.3 0.4-0.4 0.5-0.5 0.6-0.6 0.7-0.7 0.8-0.8 0.9-0.9 1.0-1.0 1.1-1.1 1.2-1.2

1.3-1.3

1.4-1.4

1.5-1.5

1.6-1.6

ATLAS

Tile Calorimeter

A sideC side

D0 D1 D2 D3D1D2D3

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A1A2A3A4A5A6A7A8A9A10

BC1 BC2 BC3 BC4 BC5 BC6 BC7 BC8

BC1BC2BC3BC4BC5BC6BC7BC8

A12 A13 A14 A15 A16

B11 B12 B13 B14 B15

C10

D5 D6

A12A13A14A15A16

B11B12B13B14B15

C10

D5D6

(a)

0.001 0.005 0.0050.0040.0110.003

0.003-0.008 -0.0010.008 -0.010 -0.011 -0.017 -0.007 0.0030.018-0.0200.006-0.0050.0100.010-0.0030.0240.028

-0.008 -0.012 -0.003 0.022 0.003 0.008 0.008

0.001

-0.015-0.006-0.005-0.010-0.005-0.001-0.003

0.006

-0.013 -0.019 -0.008 0.022 0.031

-0.027 0.008 0.004 -0.015 0.008

-0.011

-0.009

600.0500.0

-0.010-0.024-0.029-0.0090.008

0.022-0.0040.001-0.0000.009

0.017

0.004

0.0110.010

0.08−

0.06−

0.04−

0.02−

0.02

0.04

0.06

0.08

= 0.0η 0.1-0.1 0.2 3.0 2.0--0.3 0.4-0.4 0.5-0.5 0.6-0.6 0.7-0.7 0.8-0.8 0.9-0.9 1.0-1.0 1.1-1.1 1.2-1.2

1.3-1.3

1.4-1.4

1.5-1.5

1.6-1.6

ATLAS

Tile Calorimeter

A sideC side

D0 D1 D2 D3D1D2D3

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A1A2A3A4A5A6A7A8A9A10

BC1 BC2 BC3 BC4 BC5 BC6 BC7 BC8

BC1BC2BC3BC4BC5BC6BC7BC8

A12 A13 A14 A15 A16

B11 B12 B13 B14 B15

C10

D5 D6

A12A13A14A15A16

B11B12B13B14B15

C10

D5D6

(b)

Fig. 25 Visualisation of the TileCal in the (z,r) plane showing the relative difference of the ﬁtted response ratios ar(2015 −2016 →2017),

obtained in 2015–2016 and 2017, and br(2017 →2018), obtained in 2017 and 2018. The average statistical error in the determinations is about

0.8%

these events deposit most of their energy in the EM calorime-

ter [65]. The experimental results are compared with those

obtained analysing simulated data. The simulation program

Pythia 8.186 [63] was used to generate multijet events with

the A14 set of tuned parameters (tune) [67] and the NNPDF23

leading-order parton distribution function set. The detec-

tor response was simulated using the Geant4 simulation

program. The transverse momentum of the tracks was re-

weighted to match the observed spectrum in data. The same

selection criteria and signal reconstruction procedures used

in the analysis of the experimental data are used in the anal-

ysis of the simulated data.

Good agreement is found between the experimental and

simulated results. The E/pdistribution of the selected events

is shown in Fig. 26. The mean of the distribution obtained by

analysing experimental (simulated) data is E/pis 0.5896

±0.0001 (0.593 ±0.001). Only statistical uncertainties are

quoted. The values are smaller than one because of the non-

compensating nature of the calorimeter. Also energy leakage

affects the result. Figure 27 shows the values of E/pas

a function of the hadron p,ηand φ.TheE/pvariation

with pseudorapidity is associated with geometrical effects.

Approximatelly constant E/pvalues are observed in the

more uniform region in the long barrel (|η|<0.8) while

the larger variations appear in the gap/crack regions and

extended barrels of the detector. The effect also translates

in the measured E/pstandard deviation, which is around

0.28 for |η|<1.0 and increases to 0.55 for larger absolute

values of pseudorapidity. The statistical uncertainties of the

determinations are smaller than 0.1%.

The ratio of the mean E/pvalues obtained analysing

experimental and simulated events in three ηregions (|η|<

0.7, 0.7≤|η|≤1.0 and 1.0<|η|≤1.7) are reported

in Table 4. The table also reports the ratios of the standard

deviations of the E/pdistributions, σ(E/p), obtained using

experimental and simulated data. For each of three pseudo-

rapidity regions, the values refer to isolated hadrons with

momenta 2.0GeV ≤p<3.0GeV,3.0GeV ≤p<

4.0GeV,4.0GeV≤p<5.0GeV,5.0GeV≤p<

7.0 GeV and p≥7 GeV respectively.

123

Eur. Phys. J. C (2024) 84:1313 Page 29 of 53 1313

0.02

0.04

0.06

0.08

0.1

0.12

Normalised Entries

ATLAS

Tile Calorimeter

-1

= 13 TeV, 144.9 pbs

Data 2017

Pythia8

MC stat. uncert.

0.5−0 0.5 1 1.5 2 2.5

p/E

0.5

1.5

Data / MC

Fig. 26 The distribution of the ratio of the energy of isolated hadrons

measured by the calorimeters divided by the momentum of the track

measured by the inner detector (E/p). The distribution obtained by

analysing simulated data is also shown. The distributions are normalised

to an integrated area of one. The ratios of the experimental values to

the simulated ones are plotted in the lower panel. The MC statistical

uncertainties are shown

Two sources of systematic uncertainty are identiﬁed. The

ﬁrst one is from the contamination of neutral particles. The

effects on the ratio of the experimental and simulation results

are estimated by varying the selection criteria for ELAr and

ETile/(ELAr +ETile). To this end, different cuts on the recon-

structed number of clusters are also applied. A systematic

uncertainty of about 1%, almost independent of η, is found.

It corresponds to the maximum difference of the results

obtained by applying the different cuts. In particular for large

values of |η|, another source of systematic uncertainty comes

from the description in the simulation of the dead material in

front of TileCal. Effects of few percent are observed by vary-

ing the energy deposited in the gap/crack scintillators in the

dead material region. Statistical and systematic uncertain-

ties are combined in quadrature in Table 4. The comparison

between the results obtained by analysing experimental and

simulated data shows a good reconstruction of the energy at

the EM scale of low momentum hadrons. The uncertainty on

the mean value (standard deviation) of the energy distribu-

tions is better than 5% (6%).

7.3 Timing performance with collision data

Correct channel timing obtained by analysing jets as dis-

cussed in Sect. 3.1.1 is essential for energy reconstruction,

particle identiﬁcation and searches for hypothetical long-

lived particles. The performance of time calibration is pre-

sented in this section.

7.3.1 Mean time and time resolution

Figure 28 compares the mean of the cell time (average of

the reconstructed times of channels associated with that cell)

as a function of the cell energy measurements of all TileCal

cells in each of the four years of Run 2, 2015, 2016, 2017 and

2018. For each energy bin, the mean cell time corresponds

to the mean value, μ, of a Gaussian function ﬁt to the cell

time distribution in the range of μ±2σ(σis the standard

deviation of the Gaussian function). To do this, an iterative

procedure is applied. The mean cell time decreases with the

cell energy, reaching the plateau at energies above 40 GeV.

The time behaviour remains essentially unchanged during the

whole Run 2. The differences appear only in the two lowest

energy bins, below 4 GeV, dominated by pile-up conditions.

At higher energies, the year-to-year differences are within

0.05 ns in agreement with previous results [68].

Time resolution corresponds to the σof the Gaussian func-

tion ﬁt to the cell time distribution discussed above. The

values of time resolution and of the RMS of the cell time

distribution in each cell energy bin obtained using 2018 data

are shown in Fig. 29a. The time resolution is relatively sta-

ble between individual runs. The RMS values, being more

affected by the tails of the time distribution driven by the

pile-up conditions in the given run, are more unstable. The

ﬁt functions

σ=p2

0+(p1/√E)2+(p2/E)2(18)

are superimposed on HG and LG resolution data in the ﬁgure.

The ﬁt functions obtained for the four different data-taking

years of Run 2 are compared in Fig. 29b. Since the calibration

procedure for the HG setting remained unchanged during

the whole Run 2, the differences in time resolution for cell

energies smaller than 22 GeV are mainly caused by different

pile-up conditions. The best resolution is observed in 2015.

The difference relative to other years is 10% for cell energies

around 20 GeV in HG.

The time calibration for LG was the same during the years

2016–2018. The time resolution is slightly worse in 2015 due

to an older calibration procedure. It relied on the HG time

calibration taking into account a constant offset due to faster

signal propagation in LG instead of direct determination of

the LG time calibration constants. The updated procedure, as

described in Sect. 3.1.1, improves the time resolution for LG

by up to 5% despite higher pile-up in 2016–2018 compared

to 2015.

123

1313 Page 30 of 53 Eur. Phys. J. C (2024) 84:1313

0.4

0.5

0.6

0.7

0.8

0.9

〉p/E〈

ATLAS

Tile Calorimeter

-1

= 13 TeV, 144.9 pbs

> 2 GeVp

< 1.0 GeV

> 0.7

Tot.

Tile

Data 2017

Pythia8

MC stat. uncert.

0 5 10 15 20 25 30

[GeV]p

0.9

1.1

Data / MC

MC stat. uncert.

systematic uncert.

(a)

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

〉p/E〈

ATLAS

Tile Calorimeter

-1

= 13 TeV, 144.9 pbs

Data 2017

Pythia8

MC stat. uncert.

1.5−1−0.5−00.5 11.5

0.9

1.1

Data / MC

MC stat. uncert.

systematic uncert.

(b)

0.4

0.5

0.6

0.7

0.8

0.9

〉p/E〈

ATLAS

Tile Calorimeter

-1

= 13 TeV, 144.9 pbs

Data 2017

Pythia8

MC stat. uncert.

3−2−1−0123

0.9

1.1

Data / MC

MC stat. uncert.

systematic uncert.

(c)

Fig. 27 The average ratio of energy to momentum (E/p) measurements as a function of amomentum p,bpseudorapidity ηand cazimuth

angle φ. The ratios of experimental to simulated data are plotted in the lower panels. MC statistical and experimental systematic uncertainties are

shown

123

Eur. Phys. J. C (2024) 84:1313 Page 31 of 53 1313

Tabl e 4 Ratios of the mean values and of the standard deviations of

the E/pdistributions obtained from experimental and simulated data.

The results were obtained by analysing isolated hadrons with differ-

ent pseudorapidity and momentum values. Statistical and systematic

uncertainties are combined in quadrature

E/pData/E/pMC |η|<0.70.7≤|η|≤1.01.0<|η|≤1.7

2.0 GeV≤p<3.0 GeV 0.98 ±0.01 0.97 ±0.01 1.03 ±0.05

3.0 GeV≤p<4.0 GeV 0.98 ±0.01 0.97 ±0.01 1.00 ±0.03

4.0 GeV≤p<5.0 GeV 0.97 ±0.02 0.98 ±0.02 0.99±0.02

5.0 GeV≤p<7.0 GeV 0.98 ±0.01 0.96 ±0.02 0.97 ±0.02

p≥7.0 GeV 1.01 ±0.03 1.02 ±0.05 0.98 ±0.02

σ(E/p)Data/σ (E/p)MC |η|<0.70.7≤|η|≤1.01.0<|η|≤1.7

2.0 GeV≤p<3.0 GeV 1.00 ±0.03 1.04 ±0.02 1.08 ±0.05

3.0 GeV≤p<4.0 GeV 1.02 ±0.01 1.00 ±0.03 1.04 ±0.05

4.0 GeV≤p<5.0 GeV 1.00 ±0.02 1.02 ±0.02 1.05±0.04

5.0 GeV≤p<7.0 GeV 1.02 ±0.03 1.06 ±0.03 1.03 ±0.02

p≥7.0 GeV 1.00 ±0.04 0.94 ±0.06 1.03 ±0.03

020406080100120

Cell energy [GeV]

0.5−

0.5

1.5

Mean cell time [ns]

2015

2016

2017

2018

ATLAS

Tile Calorimeter

Fig. 28 The mean reconstructed cell time as a function of the cell

energy. Results obtained in Run 2 (2015, 2016, 2017 and 2018), are

shown. Statistical uncertainties are smaller than the size of the markers.

The discontinuity close to 22 GeV corresponds to the ADC high-/low-

gain transition

7.3.2 Effect of pile-up on the time resolution

The dependence of time resolution on pile-up was investi-

gated by analysing the data on a run-to-run basis. The mean

number of reconstructed primary vertices per bunch crossing

within a luminosity block, NPV , is used as a proxy for the

pile-up conditions in each run. Results are shown in Fig. 30.

Low pile-up runs show slightly better time resolution than

runs with higher pile-up. This feature is fully compatible with

the differences in the time resolution reported in Fig. 29b.

7.3.3 Run-to-run differences

The differences in the mean reconstructed time were also

investigated on run-to-run basis. These differences are due

to a phase between the LHC clock signal and the bunch-

crossings in ATLAS that can vary slightly in time. Large

run-to-run differences can negatively inﬂuence the time res-

olution integrated over several runs. While the run-to-run

RMS was rather large in 2015 (up to 0.1 ns), it signiﬁcantly

improved since the beginning of 2016 and stayed at the level

of 0.03 ns or better. This value is negligible compared to the

time resolution constant term p0shown in Fig. 29a and the

run-to-run differences have no impact on the time resolution

even if integrating over many runs.

ATLAS monitors this phase with the beam pick-up based

timing system (BPTX) [69].6The improvement in the run-to-

run RMS observed in the TileCal is in line with the automated

correction for large phases based on BPTX measurements

that was adopted in ATLAS in 2016.

7.4 Electronic and pile-up noise

The total noise per cell is calculated taking into account

two components, electronic noise and a contribution from

pile-up interactions (pile-up noise). These two contribu-

tions are added in quadrature to estimate the total noise (see

Sect. 7.4.2). The total cell noise is used as input to the topo-

logical clustering algorithm [64]. See also Sect. 7.2.

6The BPTX stations are beam position monitors installed in the LHC

tunnel 175m away from the ATLAS interaction point.

123

1313 Page 32 of 53 Eur. Phys. J. C (2024) 84:1313

0 20406080100

Cell energy [GeV]

Cell time resolution [ns]

ATLAS

Tile Calorimeter

=13 TeV, 2018s

RMS

σGaussian

high-gain fit

low-gain fit

(a)

0.4

0.6

0.8

1.2

1.4

Cell time resolution [ns]

2015

2016

2017

2018

ATLAS

Tile Calorimeter

0 20 40 60 80 100 120

Cell energy [GeV]

0.9

1.1

1.2

1.3

(2015) σ/σ

(b)

Fig. 29 a The time resolution (full circles) and RMS of the time distri-

bution (open squares) as a function of the cell energy. The results were

obtained by analysing 2018 data. Statistical uncertainties are smaller

than the size of the markers. The ﬁts of the function in Eq. (18)are

superimposed on high- and low-gain resolution data as indicated by

solid and dashed curves, respectively. bA comparison of the time reso-

lution ﬁt functions obtained by analysing 2015, 2016, 2017, 2018 data

samples [68] as a function of the cell energy. The 2017 high-gain ﬁt is

affected by a worse time resolution in the lower energy bin that leads to

slightly different ﬁt parameters p0and p1(see Eq. (18)) compared to

the results of the other years. The effect translates into an increase of the

2017 and 2015 resolution ratio near the high-gain end point. The lower

panel shows the ﬁtted time resolution obtained by analysing 2016, 2017

and 2018 data relative to the values obtained analysing 2015 data

7.4.1 Electronic noise

The electronic noise is measured by ﬂuctuations of the chan-

nel pedestal ped (Eq. (3)) and is largely independent of LHC

beam conditions. The distributions are obtained using dedi-

cated runs with no beam in the LHC. The reconstruction of

pedestal data mirrors that of the data-taking period, using the

non-iterative OF technique. The cell electronic noise is given

σelectronic =







(σ i

electronic)2(19)

where σi

electronic is the standard deviation (RMS) of the dis-

tribution of pedestal values of channel iand Ncis the num-

ber of channels in the cell. Most of the TileCal cells are

read out by two channels. Both are read out in HG and LG.

Therefore, for each cell there are four combinations (low-low,

low-high, high-low and high-high). The high-high combina-

tion is typically used when the deposited energy in a cell is

below 15 GeV while above that the low-low one is most com-

monly used. In the region around 15 GeV the combination of

low-high/high-low is used. Figure 31 shows the φ-averaged

electronic noise (RMS) for all cells as a function of η,for

the high-high gain combination, measured in a single repre-

sentative run taken in 2017. Different calorimeter layers are

presented. The average cell noise for regular cells is approx-

imately 20.5 MeV with a standard deviation of 2.5 MeV.

However, cells located in the region |η|≈1 show noise val-

ues closer to 27.5 MeV. These cells are formed by channels

physically located near the LVPS. Detailed discussions of the

electronic noise is given in Ref. [6].

7.4.2 Pile-up noise

The total noise is the sum of all sources of noise contributing

to the signals in TileCal cells. The electronic noise introduced

in Sect. 7.4.1 and the pile-up noise described in this section

are the two main contributions. Pile-up noise has two com-

ponents that lead to the overlay of multiple signals, they are

called in-time and out-of-time pile-up. In-time pile-up results

in the overlay of signals from multiple proton–proton inter-

actions within the triggered bunch crossing. On the other

hand, out-of-time pile-up is the contribution of signals from

neighbouring bunch crossings. It is present if the width of

the electrical pulse, shown in Fig. 4, is larger than the bunch

spacing. During Run 2, the TileCal operated in high pile-

up conditions with a proton bunch spacing of 25 ns used in

most runs. The mean number of interactions per bunch cross-

123

Eur. Phys. J. C (2024) 84:1313 Page 33 of 53 1313

5 101520253035

0.62

0.64

0.66

0.68

0.7

0.72

0.74

[ns]σCell time resolution

2015 2016

2017 2018

ATLAS

Tile Calorimeter

=13 TeVs

Fig. 30 The cell time resolution (σ) determined in the energy range

8GeV <Ecell <10 GeV as a function of the mean number of recon-

structed primary vertices per bunch crossing within a luminosity block,

NPV, in the run. Each point corresponds to one run and is obtained using

all the cells of the detector. In the analysis, only runs with 25 ns bunch

spacing and the number of bunches in the LHC larger than 1000 are

used. The worse resolution in 2015 is compatible with the larger run-

to-run differences provided the phase changes are not strictly coupled

to individual run boundaries, as discussed in Sect. 7.3.3

1.5−1−0.5−00.5 11.5

Electronic Noise RMS [MeV]

A-layer

BC-layer

D-layer

E-layer

ATLAS

Tile Calorimeter

Fig. 31 The φ-averaged electronic noise as a function of ηof the cell,

with both contributing read-out channels in high-gain mode. For each

cell type the average value over all modules is taken. The statistical

uncertainties are smaller than the marker size. Values are extracted

using a single representative pedestal calibration run taken in 2017.

The different cell types are shown separately for each layer. The tran-

sition between the long and extended barrels can be seen in the range

of 0.7<|η|<1.0

ing within a luminosity block, μ, ranged from about 8 to

70. At low μ, the total noise in TileCal cells is dominated

by the electronic noise component. However, for larger val-

ues of μ, the pile-up noise becomes the largest contribution

resulting from both in-time and out-of-time pile-up.

Normalised entries

4−

3−

2−

1−

1ATLAS

Tile Calorimeter

=13 TeVs

EBA A12

=122.53 MeVσ>=20 μMC16 <

=150.34 MeVσ>=30 μMC16 <

=131.61 MeVσ>=20 μData 2016 <

=161.69 MeVσ>=30 μData 2016 <

E [MeV]

600−400−200−0 200 400 600

Data/MC

0.5

1.5

2>=20μ>=20 / MC <μData <

>=30μ>=30 / MC <μData <

Fig. 32 Normalised energy distributions in cells A12 (1.1<|η|<

1.2) observed in pp collision data with 25 ns bunch spacing at √s=

13 TeV collected in 2016 in the zero-bias stream and in the Pythia 8

MC simulation with the A3 minimum-bias tune. An integration over

all modules is performed. The depicted distributions correspond to two

different pile-up conditions with μ=20 (squares) and μ=30

(circles). The ratio of the data to the MC simulation is shown in the

lower panel

Pile-up noise is studied in zero-bias data selected by a

random trigger. The trigger accepts events from collisions

occurring a ﬁxed number of LHC bunch crossings after an

accepted high-energy L1 trigger whose rate scales with lumi-

nosity [9]. This triggering provides a data sample that is not

biased by any residual signal in the calorimeter system. The

study is performed using events from several runs collected

in 2016 with 14 <μ<44 providing a number of events

comparable with available MC simulations. Minimum-bias

MC samples for pile-up noise studies were generated with

Pythia 8 using the NNPDF3.0NNLO parton distribution

fuction set and the A3 minimum-bias tune [70,71] and with

0<μ<50.

Figure 32 shows the energy distribution in A12 cells

located in the 1.1<|η|<1.2 region of the detector.

Those cells receive the highest radiation dose in layer A (see

Fig. 18). The energy distributions are centred around zero

in absence of high-energy signals. Increasing pile-up widens

the energy distribution both in data and MC simulation. Rea-

sonable agreement between data and the simulation is found

above approximately 200 MeV. However, below this energy,

the simulated energy distribution is narrower than in data.

The total noise measured as the standard deviation (RMS) of

123

1313 Page 34 of 53 Eur. Phys. J. C (2024) 84:1313

1.5−1−0.5−00.5 11.5

Noise [MeV]

100

200

300

1000

2000 ATLAS

Tile Calorimeter

>=30μ=13 TeV <s

Data MC

Layer A

Layer BC

Layer D

Layer E

Fig. 33 The total noise in TileCal cells, as a function of η, observed in

pp collision data with 25 ns bunch spacing at √s=13 TeV collected

in 2016 in the zero-bias stream with an average number of interactions

μ=30 per bunch crossing and in the Pythia 8 MC simulation

with the A3 minimum-bias tune. The noise is estimated as the standard

deviation (RMS) of the measured cell energy distribution. The data

(MC simulation) are plotted with closed (open) markers. The cells of

different layers are shown with different colours

the total energy distribution in A12 cells is lower by about

7% in MC simulation compared to data.

Figure 33 shows the total noise (electronic noise and the

contribution from pile-up) in different TileCal layers, for

events with μ=30, as the function of ηobtained using

experimental and simulated data. The electronic noise com-

ponent is small and almost ﬂat for standard cells, as seen in

Fig. 31. Due to the distribution of upstream material and the

distance of cells from the interaction point, the pile-up seen

in the TileCal is not expected to be uniform. The total noise is

approximately constant with ηin the LB, while the variations

in the EB are due to larger amounts of various absorbers tra-

versed by soft particles. The largest effect of pile-up is seen in

the cells of layers A and E that are the closest to the collision

point and experience the highest particle ﬂux (see Fig. 18).

The pile-up noise diminishes for cells in the outermost layers

BC/B and D. The simulation models the ηdependence of the

noise. However, for all ηvalues, the simulation predicts 20%

lower noise than the data as shown in Fig. 33. The largest

part of the difference comes from the negative energy tail

seen in Fig. 32, which is a feature of the energy reconstruc-

tion caused by out-of-time pile-up. The total noise measured

in MC is used as input to the topological clustering algorithm

for data and MC.

The pile-up noise is measured in all TileCal cells using

the dependence of the total noise on μ. As an example the

results of the experimental and simulated data for the cells

A5, B5, D2, A13, B13, D6 and E3 are shown in Fig. 34.The

results for cells of the same type located in Side C (η<0)

and Side A (η>0) are averaged assuming similar pile-up in

both sides of the TileCal. The total noise, σtotal, distribution

is ﬁtted using the function:

σtotal =σ2

electronic +μσ2

pile-up (20)

where σelectronic and σpile-up are the electronic and pile-

up noise respectively. The statistical errors in the measured

total noise are obtained using pseudo experiments from MC

simulation [72]. The use of statistical errors in the ﬁt increases

its reliability. Electronic noise σelectronic that corresponds to

μ=0, is measured in pedestal calibration data as dis-

cussed in Sect. 7.4.1. Pile-up noise is derived from a ﬁt using

σpile-up as the free parameter. The ﬁt functions are overlayed

on the experimental and simulated data points in the ﬁgure.

The experimental (simulated) determinations of σpile-up are

25.5 (23.0) MeV for cell A5, 16.5 (14.2) MeV for the cell

BC5, 4.5 (2.95) MeV for the cell D2, 43.9 (40.5) MeV for

cell A13, 18.2 (15.1) MeV for cell B13, 7.54 (6.41) MeV for

cell D6 and 129.0 (100.6) MeV for cell E3.

The determined pile-up noise is uploaded to the conditions

database, for use by the physics object reconstruction algo-

rithms to calculate total noise for any μcondition. This is

used online by the HLT and ofﬂine by the algorithm building

topological clusters used to reconstruct jets [64].

7.5 Performance of the tile muon trigger system

The tile muon boards, described in Sect. 4.2, were installed

and integrated into the ATLAS data acquisition system dur-

ing the LHC technical stop in 2015. The system has been

operating since the beginning of 2018. This section presents

results of the tile muon system performance studies using

data collected in 2018.

The ηdistribution obtained using the tile muon trigger

can be compared with the one obtained using the L1 muon

trigger in Fig. 35. The data show that the tile muon trig-

ger coincidence reduces the acceptance rate in the region

1.0<|η|<1.3 by about 50%. The total muon trigger rate is

reduced by about 6%. Studies made using Z→μμ events

show that the tile muon trigger inefﬁciency is less than 2.5%,

compatible with the expected geometrical inefﬁciency due to

the thin gaps between TileCal modules [37].

7.6 Performance of the minimum-bias trigger scintillators

The events used to study the response of the TileCal to iso-

lated hadrons (Sect. 7.2) were collected in 2017, more than

two years after the start of Run 2. As already discussed they

were selected with the MBTS system (Sect. 4.3) requiring a

signal over threshold in at least one MBTS counter in either

of the two sides of the detector.

The MBTS trigger efﬁciency is measured as a function

of the multiplicity of tracks reconstructed in the inner detec-

123

Eur. Phys. J. C (2024) 84:1313 Page 35 of 53 1313

>μ<

0 1020304050

Noise [MeV]

100

120

140

160

180

ATLAS

Tile Calorimeter

=13 TeVs

Data MC Fit

LBA cells:

BC5

(a)

>μ<

0 1020304050

Noise [MeV]

100

200

300

400

500

600

700

800

900

ATLAS

Tile Calorimeter

=13 TeVs

Data MC Fit

EBA cells:

A13

B13

(b)

Fig. 34 The total noise in TileCal cells, as a function of the average

number of interactions per bunch crossing μ, observed in pp collision

data with 25 ns bunch spacing at √s=13 TeV collected in 2016 in

the zero-bias stream with an average number of μequal to 30, and in

Pythia 8 MC simulation with the A3 minimum-bias tune. The noise is

estimated as the standard deviation of the energy distribution per cell.

The data (MC simulation) are plotted with closed (open) markers. The

noise is shown for cells located in the region a0.4<|η|<0.5inthe

LB and b1.2<|η|<1.3 in the EB. Due to statistical limitations, the

total noise measured in data is shown only for μ>15. The ﬁt func-

tions from Eq. (20) are overlayed on the experimental and simulated

data points (dashed curves) in the ﬁgure

Fig. 35 The ηdistribution of particles with transverse momentum

larger than 20 GeV measured in events selected using the L1 muon

trigger alone (triangles) and the tile muon system coincidence (line).

The ratios of the bin contents are shown in the lower panel. The coinci-

dence regions, 1.0<|η|<1.3, are indicated by the vertical lines. The

ηof the tracks is reconstructed using online information. The asymme-

try of the distribution is due to the different acceptance of the muon

spectrometer within the toroidal magnetic ﬁeld

tor [73]. Figure 36a shows the results obtained using data

collected in 2015 at the beginning of Run 2. The trigger

efﬁciency of the events with only one charged particle with

pT>500 MeV emitted in the range of |η|<2.5isalmost

99%. It reaches 100% for events with more than 10 particles.

At the same time, only about 20% of events triggered by the

MBTS do not have reconstructed tracks, which means that

the fraction of false triggers is even lower, since this cate-

gory also contains real pp interactions where no particles

are produced in the acceptance of the inner detector. The

measurements were performed after a long shutdown period

in which new scintillating counters were installed and the

performance of the detector was not yet affected by the radi-

ation effects discussed in Sect. 5. In 2015, the PMT HV was

set around 700 V, and the counter threshold value was about

100 mV. In 2017, after more than two years of operation, the

MBTS counter response degraded and the data used to study

the response of the TileCal to isolated hadrons (see Sect. 7.2)

were collected with the PMTs HV increased to 750–800 V

and the threshold value reduced down to around 50 mV. As

shown in Fig. 36b the trigger efﬁciency drops from 99% to

80% for events with the lowest multiplicity. It is about 95%

for events with 10 charged particles and does not reach 100%

even for events with 40 charged particles.

7.7 Summary of performance studies

Muons produced by W→μν decays in pp collisions

were used to check the calibration of the TileCal. The non-

uniformity of the cell response, due to variations in the optical

components, electronics of the read-out systems and potential

cell miscalibration, is determined. A value of 2.4% is found

by analysing the events of the three periods 2015–2016, 2017

and 2018. The analyses show no clear evidence of problem-

atic cells. The radial layer intercalibration of the TileCal was

123

1313 Page 36 of 53 Eur. Phys. J. C (2024) 84:1313

246810

sel

0.985

0.99

0.995

Trigger efficiency

ATLAS

= 13 TeVs

| < 2.5η > 500 MeV, |

1, p≥

sel

Data 2015

(a)

5 10152025303540

sel

0.7

0.8

0.9

Trigger efficiency

ATLAS

= 13 TeVs

| < 2.5η > 500 MeV, |

1, p≥

sel

Data 2015

Data 2017

(b)

Fig. 36 a The efﬁciency of the MBTS trigger during pp collisions at

√s=13 TeV as a function of the multiplicity of charged particles com-

patible with the beam line, nBL

sel , with pT>500 MeV and |η|<2.5

reconstructed in the inner detector. The data were collected in 2015. The

statistical uncertainties are shown as vertical bars, the sum in quadra-

ture of statistical and systematic uncertainties is shown as the height of

the shaded rectangles [73]. Systematic uncertainties dominate. bThe

efﬁciency of the MBTS trigger during pp collisions at √s=13 TeV as

a function of the multiplicity of charged particles compatible with the

beam line, nBL

sel ,withpT>500 MeV and |η|<2.5 reconstructed in the

inner detector. Results from the analysis of data collected in 2017 are

compared with those from 2015 presented in a. The statistical uncer-

tainties are shown as vertical bars, the sum in quadrature of statistical

and systematic uncertainties of the 2017 data are shown by the shaded

rectangles. For 2017 data, statistical uncertainties dominate, while the

uncertainties for 2015 data in bare smaller than the size of the symbols

because of the much larger range of the vertical axis than in a

also evaluated. For all layers the results are consistent with a

correct setting of the scale used to reconstruct the jet energy

within 2% in all three periods. Good stability of the detec-

tor response over time was observed. The studies show 95%

conﬁdence intervals for the variations of the cell response

between 2015–2016 and 2017 and between 2017 and 2018

of [−1.3%, 3.3%] and [−2.4%, 2.5%], respectively.

The measurements of the E/pratio using isolated hadrons

show good uniformity of the response across the azimuth

angle φ. An agreement between experimental and simulated

results is observed, conﬁrming the goodness of the calibra-

tion of the cell energy at the EM scale. The uncertainty on

the determinations of the mean value (standard deviation) is

better than 5% (6%).

The time calibration was extensively checked and moni-

tored during Run 2. Using jet measurements, the resolution

of the time determination in the TileCal cells is found to be

better than 1 ns for energies deposited in the cells larger than

5 GeV. The huge reduction of the timing jumps, the most

critical issue in Run 1, allowed the investigation of and the

correction for other timing-related problems as speciﬁed in

Sect. 3.1.1. In the HG regime, despite signiﬁcantly larger

pile-up, a slightly better time resolution is obtained in Run 2.

The time resolution for LG beneﬁts from an improved cali-

bration procedure.

Noise measurements were compared with the simulated

expectations. In the case of the A12 cells that receive the

highest radiation dose in the regular TileCal cells, the noise

is lower by about 7% in MC simulation compared to data.

In 2015, at the beginning of Run 2, the MBTS trigger

efﬁciency was measured to be larger than 98.5%. In 2017,

the responses of the radiation-damaged detectors were found

to be 0.5% (inner counters) and 10% (outer counters) of the

corresponding values measured in 2015. Despite this large

response deterioration, in 2017 the MBTS trigger efﬁciency

was larger than 80%.

The tile muon trigger system uses the TileCal informa-

tion combined with the TGC muon chambers to reduce the

background muon trigger rate due to low momentum pro-

tons emerging from the endcap toroid and the beam shield-

ing. The total muon trigger rate reduces by about 6%. The

Z→μμ events show that the tile muon trigger efﬁciency

is about 97.5%, compatible with the expected geometrical

inefﬁciency due to thin gaps between TileCal modules [60].

8 Conclusion

This paper presents a description of the ATLAS tile calorime-

ter signal reconstruction, calibration, data quality monitor-

ing, and performance during the Run 2 data-taking period of

the LHC (2015–2018).

A precision of about 1% was demonstrated for the indi-

vidual calorimeter calibration systems. Information collected

123

Eur. Phys. J. C (2024) 84:1313 Page 37 of 53 1313

from the D layer of the EB and the MBTS used for trigger

purposes have been presented. The investigation of ageing

and radiation damage effects allows the expected light yield

of the calorimeter to be extrapolated to the end of the data-

taking period at the LHC (HL-LHC). The cells exposed to

the most severe radiation conditions are expected to degrade

to 75+9

−25%(35

+17

−27%). The radiation damage to the MBTS is

also assessed. Data quality activities lead the TileCal to con-

tribute with an efﬁciency of 99.65% to high-quality ATLAS

data-taking and with less than 1.1% of all cells deemed non-

operational at the end of each data-taking year. Thorough

monitoring of cell performance allows for quick and efﬁcient

repairs during yearly maintenance periods.

Performance is assessed with isolated muons, hadrons and

jets. The proton–proton collisions at the LHC at a centre-of-

mass energy of 13 TeV are used in the analyses. The results

obtained by analysing muon data are consistent with a cor-

rect setting of the EM scale used to reconstruct the jet energy

within 2% during all of the Run 2 period. The MC modelling

of the single pion energy response is shown to agree with

the data collected within the assigned uncertainties. The tim-

ing performance determined from jet measurements is stable

during Run 2. Compared to Run 1, the time resolution shows

small increase due to pile-up effects, but stays well below the

0.7 ns above 10 GeV in high-gain. Large increases in noise,

coming from increased pile-up conditions during Run 2, are

extracted and modelled for each TileCal cell. Despite radia-

tion damage, the MBTS allows the online selection of events

with the highest efﬁciency and the lowest possible bias, as

well as calorimeter performance studies. Coincidence of the

D cells of the EB with the muon trigger system results in

a 50% reduction of the excess trigger rates induced by low

energy background protons.

Overall, during the LHC Run 2, the TileCal performed in

accordance with expectations.

Acknowledgements We thank CERN for the very successful oper-

ation of the LHC and its injectors, as well as the support staff

at CERN and at our institutions worldwide without whom ATLAS

could not be operated efﬁciently. The crucial computing support from

all WLCG partners is acknowledged gratefully, in particular from

CERN, the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF

(Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Ger-

many), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL

(UK) and BNL (USA), the Tier-2 facilities worldwide and large non-

WLCG resource providers. Major contributors of computing resources

are listed in Ref. [74]. We gratefully acknowledge the support of

ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW

and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil;

NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST

and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Repub-

lic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU,

France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI,

Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Cen-

ter, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco;

NWO, Netherlands; RCN, Norway; FCT, Portugal; MNE/IFA, Roma-

nia; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slove-

nia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg

Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva,

Switzerland; MOST, Taipei; TENMAK, Türkiye; STFC, UK; DOE and

NSF, United States of America. Individual groups and members have

received support from BCKDF, CANARIE, CRC and DRAC, Canada;

CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Repub-

lic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and

Marie Skłodowska-Curie Actions, European Union; Investissements

d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG

and AvH Foundation, Germany; Herakleitos, Thales and Aristeia pro-

grammes co-ﬁnanced by EU-ESF and the Greek NSRF, Greece; BSF-

NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-

2021, Norway; NCN and NAWA, Poland; La Caixa Banking Founda-

tion, CERCA Programme Generalitat de Catalunya and PROMETEO

and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafs-

sons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, UK.

In addition, individual members wish to acknowledge support from

Chile: Agencia Nacional de Investigación y Desarrollo (FONDECYT

1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT

1230987); China: National Natural Science Foundation of China (NSFC

- 12175119, NSFC 12275265, NSFC-12075060); Czech Republic:

PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020

European Research Council (ERC - 101002463); European Union:

European Research Council (ERC - 948254), Horizon 2020 Framework

Programme (MUCCA - CHIST-ERA-19-XAI-00), European Union,

Future Artiﬁcial Intelligence Research (FAIR-NextGenerationEU

PE00000013), Italian Center for High Performance Computing, Big

Data and Quantum Computing (ICSC, NextGenerationEU), Marie

Sklodowska-Curie Actions (EU H2020 MSC IF GRANT NO 1010334

-96); France: Agence Nationale de la Recherche (ANR-20-CE31-0013,

ANR-21-CE31-0013, ANR-21-CE31-0022), Investissements d’Avenir

Idex (ANR-11-LABX-0012), Investissements d’Avenir Labex (ANR-

11-LABX-0012); Germany: Baden-WürttembergStiftung (BW Stiftung-

Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG -

469666862, DFG - CR 312/5-1); Italy: Istituto Nazionale di Fisica

Nucleare (FELLINI G.A. n. 754496, ICSC, NextGenerationEU);

Japan: Japan Society for the Promotion of Science (JSPS KAK-

ENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAK-

ENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands:

Netherlands Organisation for Scientiﬁc Research (NWO Veni 2020

- VI.Veni.202.179); Norway: Research Council of Norway (RCN-

314472); Poland: Polish National Agency for Academic Exchange

(PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre

(NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059,

NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-

2021/40/C/ST2/00187); Slovenia: Slovenian Research Agency (ARIS

grant J1-3010); Spain: BBVA Foundation (LEO22-1-603), Generali-

tat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), La Caixa

Banking Foundation (LCF/BQ/PI20/11760025), Ministry of Science

and Innovation (MCIN & NextGenEU PCI2022-135018-2, MICIN &

FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-

I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT

Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDE-

GENT/2019/027); Sweden: Swedish Research Council (VR 2018-

00482, VR 2022-03845, VR 2022-04683, VR grant 2021-03651), Knut

and Alice Wallenberg Foundation (KAW 2017.0100, KAW 2018.0157,

KAW 2018.0458, KAW 2019.0447); Switzerland: Swiss National Sci-

ence Foundation (SNSF - PCEFP2_194658); UK: Leverhulme Trust

(Leverhulme Trust RPG-2020-004); USA: U.S. Department of Energy

(ECA DE-AC02-76SF00515), Neubauer Family Foundation.

Data Availability Statement This manuscript has no associated data.

[Authors’ comment: All ATLAS scientiﬁc output is published in jour-

nals, and preliminary results are made available in Conference Notes.

All are openly available, without restriction on use by external parties

beyond copyright law and the standard conditions agreed by CERN.

123

1313 Page 38 of 53 Eur. Phys. J. C (2024) 84:1313

Data associated with journal publications are also made available: tables

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tion efﬁciencies, cross section limits, ...) are stored in appropriate repos-

itories such as HEPDATA (http://hepdata.cedar.ac.uk/). ATLAS also

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cal models. For example, an extended encapsulation of the analysis is

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Access Policy, which is a public document that can be downloaded from

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Code Availability Statement This manuscript has no associated

code/software. [Authors’ comment: ATLAS collaboration software is

open source, and all code necessary to recreate an analysis is pub-

licly available. The Athena (http://gitlab.cern.ch/atlas/athena)software

repository provides all code needed for calibration and uncertainty

application, with conﬁguration ﬁles that are also publicly available via

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C. Chauhan133 , S. Chekanov6, S. V. Chekulaev156a , G. A. Chelkov38,a, A. Chen106 , B. Chen151 ,
B. Chen165 , H. Chen14c , H. Chen29 , J. Chen62c , J. Chen142 , M. Chen126 , S. Chen153 , S.J.Chen
14c ,
X. Chen62c,135 , X. Chen14b,af , Y. Chen62a , C. L. Cheng170 , H. C. Cheng64a , S. Cheong143 , A. Cheplakov38 ,
E. Cheremushkina48 , E. Cherepanova114 , R. Cherkaoui El Moursli35e , E. Cheu7, K. Cheung65 , L. Chevalier135 ,
V. Chiarella53 , G. Chiarelli74a , N. Chiedde102 , G. Chiodini70a , A. S. Chisholm20 , A. Chitan27b ,
M. Chitishvili163 , M. V. Chizhov38 , K. Choi11 , A. R. Chomont75a,75b , Y. Chou103 , E.Y.S.Chow
113 ,
T. Chowdhury33g ,K.L.Chu
169 , M.C.Chu
64a ,X.Chu
14a,14e , J. Chudoba131 , J. J. Chwastowski87 ,
D. Cieri110 ,K.M.Ciesla
86a , V. Cindro93 , A. Ciocio17a , F. Cirotto72a,72b , Z. H. Citron169,k,
M. Citterio71a , D. A. Ciubotaru27b,A.Clark
56 , P.J.Clark
52 ,C.Clarry
155 , J. M. Clavijo Columbie48 ,
S. E. Clawson48 , C. Clement47a ,47b ,J.Clercx
48 , Y. Coadou102 , M. Cobal69a ,69c , A. Coccaro57b ,
R. F. Coelho Barrue130a , R. Coelho Lopes De Sa103 , S. Coelli71a , A.E.C.Coimbra
71a,71b ,B.Cole
41 ,
J. Collot60 , P. Conde Muiño130a,130g , M. P. Connell33c , S. H. Connell33c , I. A. Connelly59 , E.I.Conroy
126 ,
F. Conventi72a,ah , H.G.Cooke
20 , A. M. Cooper-Sarkar126 , A. Cordeiro Oudot Choi127 , L. D. Corpe40 ,
M. Corradi75a,75b , F. Corriveau104,x, A. Cortes-Gonzalez18 , M.J.Costa
163 , F. Costanza4, D. Costanzo139 ,
B. M. Cote119 ,G.Cowan
95 , K. Cranmer170 , D. Cremonini23a ,23b , S. Crépé-Renaudin60 , F. Crescioli127 ,
M. Cristinziani141 , M. Cristoforetti78a,78b ,V.Croft
114 , J. E. Crosby121 , G. Crosetti43a,43b , A. Cueto99 ,
T. Cuhadar Donszelmann159 ,H.Cui
14a,14e ,Z.Cui
7, W. R. Cunningham59 , F. Curcio43a,43b , P. Czodrowski36 ,
M. M. Czurylo63b , M. J. Da Cunha Sargedas De Sousa57a,57b , J. V. Da Fonseca Pinto83b ,C.DaVia
101 ,
W. Dabrowski86a , T. Dado49 , S. Dahbi33g ,T.Dai
106 , D. Dal Santo19 , C. Dallapiccola103 ,M.Dam
42 ,
G. D’amen29 , V. D’Amico109 ,J.Damp
100 , J. R. Dandoy34 , M. Danninger142 ,V.Dao
36 , G. Darbo57b ,
S. Darmora6, S.J.Das
29,aj ,S.D’Auria
71a,71b ,C.David
156b , T. Davidek133 , B. Davis-Purcell34 ,
I. Dawson94 , H. A. Day-hall132 ,K.De
8, R. De Asmundis72a ,N.DeBiase
48 ,S.DeCastro
23a,23b ,
N. De Groot113 , P. de Jong114 ,H.DelaTorre
115 ,A.DeMaria
14c , A. De Salvo75a , U. De Sanctis76a,76b ,
F. De Santis70a,70b , A. De Santo146 ,J.B.DeVivieDeRegie
60 , D. V. Dedovich38, J. Degens114 , A. M. Deiana44 ,
F. Del C o r so23a,23b , J. Del Peso99 ,F.DelRio
63a , L. Delagrange127 , F. Deliot135 , C. M. Delitzsch49 ,
M. Della Pietra72a,72b , D. Della Volpe56 , A. Dell’Acqua36 , L. Dell’Asta71a,71b , M. Delmastro4,
P. A. Delsart60 , S. Demers172 , M. Demichev38 , S.P.Denisov
37 , L. D’Eramo40 , D. Derendarz87 ,
F. Der u e 127 ,P.Dervan
92 , K. Desch24 , C. Deutsch24 , F.A.DiBello
57a,57b , A. Di Ciaccio76a,76b ,
L. Di Ciaccio4, A. Di Domenico75a ,75b , C. Di Donato72a,72b , A. Di Girolamo36 , G. Di Gregorio36 ,
A. Di Luca78a,78b , B. Di Micco77a,77b , R.DiNardo
77a,77b , C. Diaconu102 , M. Diamantopoulou34 ,
F. A. Dias114 ,T.DiasDoVale
142 ,M.A.Diaz
137a,137b , F. G. Diaz Capriles24 , M. Didenko163 , E. B. Diehl106 ,
123

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L. Diehl54 , S. Díez Cornell48 , C. Diez Pardos141 , C. Dimitriadi161,24 , A. Dimitrievska17a , J. Dingfelder24 ,
I-M. Dinu27b , S. J. Dittmeier63b , F. Dittus36 ,F.Djama
102 , T. Djobava149b , C. Doglioni101,98 ,
A. Dohnalova28a , J. Dolejsi133 , Z. Dolezal133 , K. M. Dona39 , M. Donadelli83c , B. Dong107 ,
J. Donini40 , A. D’Onofrio72a ,72b , M. D’Onofrio92 , J. Dopke134 ,A.Doria
72a , N. Dos Santos Fernandes130a ,
P. Dougan101 , M.T.Dova
90 ,A.T.Doyle
59 , M. A. Draguet126 , E. Dreyer169 , I. Drivas-koulouris10 ,
M. Drnevich117 , A. S. Drobac158 , M. Drozdova56 ,D.Du
62a ,T.A.duPree
114 , F. Dubinin37 ,
M. Dubovsky28a , E. Duchovni169 , G. Duckeck109 , O. A. Ducu27b , D. Duda52 , A. Dudarev36 ,
E. R. Duden26 , M. D’ufﬁzi101 , L. Duﬂot66 , M. Dührssen36 , A. E. Dumitriu27b , M. Dunford63a ,
S. Dungs49 , K. Dunne47a,47b , A. Duperrin102 , H. Duran Yildiz3a , M. Düren58 , A. Durglishvili149b ,
B. L. Dwyer115 , G. I. Dyckes17a , M. Dyndal86a , B. S. Dziedzic87 , Z. O. Earnshaw146 , G. H. Eberwein126 ,
B. Eckerova28a , S. Eggebrecht55 , E. Egidio Purcino De Souza127 ,L.F.Ehrke
56 ,G.Eigen
16 , K. Einsweiler17a ,
T. Ekelof161 , P. A. Ekman98 , S. El Farkh35b , Y. El Ghazali35b , H.ElJarrari
36 , A. El Moussaouy108 ,
V. Ellajosyula161 , M. Ellert161 , F. Ellinghaus171 , N. Ellis36 , J. Elmsheuser29 , M. Elsing36 , D. Emeliyanov134 ,
Y. Enari153 ,I.Ene
17a , S. Epari13 , P. A. Erland87 , M. Errenst171 , M. Escalier66 , C. Escobar163 ,
E. Etzion151 , G. Evans130a , H. Evans68 , L. S. Evans95 , M. O. Evans146 , A. Ezhilov37 , S. Ezzarqtouni35a ,
F. Fabbri59 , L. Fabbri23a,23b , G. Facini96 , V. Fadeyev136 , R. M. Fakhrutdinov37 , D. Fakoudis100 ,
S. Falciano75a , L. F. Falda Ulhoa Coelho36 ,P.J.Falke
24 , J. Faltova133 ,C.Fan
162 ,Y.Fan
14a , Y. Fang14a,14e ,
M. Fanti71a,71b , M. Faraj69a,69b , Z. Farazpay97 , A. Farbin8, A. Farilla77a , T. Farooque107 , S. M. Farrington52 ,
F. Fassi35e , D. Fassouliotis9, M. Faucci Giannelli76a,76b , W.J.Fawcett
32 , L. Fayard66 , P. Federic133 ,
P. Federicova131 , O. L. Fedin37,a, G. Fedotov37 , M. Feickert170 , L. Feligioni102 , D.E.Fellers
123 ,
C. Feng62b , M. Feng14b , Z. Feng114 , M. J. Fenton159 , A. B. Fenyuk37, L. Ferencz48 , R. A. M. Ferguson91 ,
S. I. Fernandez Luengo137f , P. Fernandez Martinez13 , M. J. V. Fernoux102 , J. Ferrando91 , A. Ferrari161 ,
P. Ferrari114 ,113 , R. Ferrari73a , D. Ferrere56 , C. Ferretti106 , F. Fiedler100 , P. Fiedler132 , A. Filipˇciˇc93 ,
E. K. Filmer1, F. Filthaut113 , M.C.N.Fiolhais
130a,130c,c,L.Fiorini
163 , W. C. Fisher107 , T. Fitschen101 ,
P. M. Fitzhugh135, I. Fleck141 , P. Fleischmann106 , T. Flick171 ,M.Flores
33d,ad , L. R. Flores Castillo64a ,
L. Flores Sanz De Acedo36 , F. M. Follega78a,78b ,N.Fomin
16 , J.H.Foo
155 , B. C. Forland68, A. Formica135 ,
A. C. Forti101 , E. Fortin36 , A.W.Fortman
61 , M.G.Foti
17a , L. Fountas9,j, D. Fournier66 ,H.Fox
91 ,
P. Francavilla74a,74b , S. Francescato61 , S. Franchellucci56 , M. Franchini23a,23b , S. Franchino63a , D. Francis36,
L. Franco113 , V. Franco Lima36 , L. Franconi48 , M. Franklin61 , G. Frattari26 ,A.C.Freegard
94 ,
W. S. Freund83b ,Y.Y.Frid
151 , J. Friend59 , N. Fritzsche50 , A. Froch54 , D. Froidevaux36 ,J.A.Frost
126 ,
Y. Fu 62a , S. Fuenzalida Garrido137f , M. Fujimoto102 , K. Y. Fung64a , E. Furtado De Simas Filho83b ,
M. Furukawa153 , J. Fuster163 , A. Gabrielli23a,23b , A. Gabrielli155 , P. Gadow36 , G. Gagliardi57a,57b ,
L. G. Gagnon17a , B. Galhardo130a , E. J. Gallas126 , B.J.Gallop
134 ,K.K.Gan
119 , S. Ganguly153 ,
Y. Ga o52 , F. M. Garay Walls137a,137b , B. Garcia29, C. García163 , A. Garcia Alonso114 , A. G. Garcia Caffaro172 ,
J. E. García Navarro163 , M. Garcia-Sciveres17a , G. L. Gardner128 , R. W. Gardner39 , N. Garelli158 ,
D. Garg80 ,R.B.Garg
143,n, J.M.Gargan
52, C. A. Garner155, C. M. Garvey33a , P. Gaspar83b , V. K. Gassmann158,
G. Gaudio73a , V. Gautam13, P. Gauzzi75a ,75b , I.L.Gavrilenko
37 , A. Gavrilyuk37 ,C.Gay
164 , G. Gaycken48 ,
E. N. Gazis10 , A. A. Geanta27b ,C.M.Gee
136 ,A.Gekow
119, C. Gemme57b , M. H. Genest60 , S. Gentile75a,75b ,
A. D. Gentry112 , S. George95 , W. F. George20 , T. Geralis46 , P. Gessinger-Befurt36 ,M.E.Geyik
171 ,
M. Ghani167 , M. Ghneimat141 , K. Ghorbanian94 , A. Ghosal141 , A. Ghosh159 , A. Ghosh7, B. Giacobbe23b ,
S. Giagu75a,75b , T. Giani114 , P. Giannetti74a , A. Giannini62a ,S.M.Gibson
95 , M. Gignac136 ,
D. T. Gil86b , A. K. Gilbert86a , B.J.Gilbert
41 , D. Gillberg34 , G. Gilles114 , N. E. K. Gillwald48 ,
L. Ginabat127 , D.M.Gingrich
2,ag , M. P. Giordani69a ,69c , P.F.Giraud
135 , G. Giugliarelli69a,69c ,
D. Giugni71a , F. Giuli36 , I. Gkialas9,j, L. K. Gladilin37 ,C.Glasman
99 , G.R.Gledhill
123 ,G.Glemža
48 ,
M. Glisic123, I. Gnesi43b,f,Y.Go
29,aj , M. Goblirsch-Kolb36 , B. Gocke49 , D. Godin108, B. Gokturk21a ,
S. Goldfarb105 , T. Golling56 , M.G.D.Gololo
33g , D. Golubkov37 , J. P. Gombas107 , A. Gomes130a,130b ,
G. Gomes Da Silva141 , A. J. Gomez Delegido163 , R. Gonçalo130a,130c , G. Gonella123 , L. Gonella20 ,
A. Gongadze149c , F. Gonnella20 , J. L. Gonski41 , R. Y. González Andana52 , S. González de la Hoz163 ,
R. Gonzalez Lopez92 , C. Gonzalez Renteria17a , M. V. Gonzalez Rodrigues48 , R. Gonzalez Suarez161 ,
S. Gonzalez-Sevilla56 , G. R. Gonzalvo Rodriguez163 , L. Goossens36 ,B.Gorini
36 ,E.Gorini
70a,70b ,
A. Gorišek93 , T.C.Gosart
128 , A.T.Goshaw
51 , M.I.Gostkin
38 ,S.Goswami
121 , C. A. Gottardo36 ,
S. A. Gotz109 , M. Gouighri35b , V. Goumarre48 , A.G.Goussiou
138 , N. Govender33c , I. Grabowska-Bold86a ,
K. Graham34 ,E.Gramstad
125 , S. Grancagnolo70a,70b , M. Grandi146 , C.M.Grant
1,135, P. M. Gravila27f ,
123

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F. G. Gravili70a,70b ,H.M.Gray
17a , M. Greco70a ,70b ,C.Grefe
24 , I. M. Gregor48 , P. Grenier143 ,S.G.Grewe
110,
C. Grieco13 , A. A. Grillo136 ,K.Grimm
31 , S. Grinstein13,t,J.-F.Grivaz
66 , E. Gross169 , J. Grosse-Knetter55 ,
C. Grud106, J. C. Grundy126 , L. Guan106 , W. Guan29 , C. Gubbels164 , J.G.R.GuerreroRojas
163 ,
G. Guerrieri69a,69c , F. Guescini110 , R. Gugel100 , J.A.M.Guhit
106 , A. Guida18 , E. Guilloton167,134 ,
S. Guindon36 ,F.Guo
14a,14e ,J.Guo
62c ,L.Guo
48 ,Y.Guo
106 , R. Gupta48 , R. Gupta129 ,S.Gurbuz
24 ,
S. S. Gurdasani54 , L. Gurriana130a , G. Gustavino36 ,M.Guth
56 , P. Gutierrez120 , L. F. Gutierrez Zagazeta128 ,
M. Gutsche50 , C. Gutschow96 , C. Gwenlan126 , C. B. Gwilliam92 , E. S. Haaland125 , A. Haas117 ,
M. Habedank48 , C. Haber17a , H. K. Hadavand8, A. Hadef50 , S. Hadzic110 , A.I.Hagan
91 , J.J.Hahn
141 ,
E. H. Haines96 , M. Haleem166 ,J.Haley
121 ,J.J.Hall
139 , G. D. Hallewell102 ,L.Halser
19 , K. Hamano165 ,
M. Hamer24 , G. N. Hamity52 , E. J. Hampshire95 ,J.Han
62b ,K.Han
62a ,L.Han
14c ,L.Han
62a ,
S. Han17a , Y.F.Han
155 , K. Hanagaki84 , M. Hance136 , D. A. Hangal41,ac , H. Hanif142 , M. D. Hank128 ,
J. B. Hansen42 , J. D. Hansen42 , P. H. Hansen42 ,K.Hara
157 , D. Harada56 , T. Harenberg171 , S. Harkusha37 ,
M. L. Harris103 ,Y.T.Harris
126 , J. Harrison13 , N.M.Harrison
119 ,P.F.Harrison
167, N.M.Hartman
110 ,
N. M. Hartmann109 , Y. Hasegawa140 , R. Hauser107 ,C.M.Hawkes
20 , R. J. Hawkings36 , Y. Hayashi153 ,
S. Hayashida111 , D. Hayden107 , C. Hayes106 , R. L. Hayes114 , C. P. Hays126 ,J.M.Hays
94 , H. S. Hayward92 ,
F. He62a ,M.He
14a,14e ,Y.He
154 ,Y.He
48 , N. B. Heatley94 , V. Hedberg98 , A. L. Heggelund125 ,
N. D. Hehir94,*, C. Heidegger54 , K. K. Heidegger54 , W. D. Heidorn81 , J. Heilman34 ,S.Heim
48 ,T.Heim
17a ,
J. G. Heinlein128 , J. J. Heinrich123 , L. Heinrich110 ,ae , J. Hejbal131 , L. Helary48 ,A.Held
170 , S. Hellesund16 ,
C. M. Helling164 , S. Hellman47a ,47b , R. C. W. Henderson91, L. Henkelmann32 , A. M. Henriques Correia36,
H. Herde98 , Y. Hernández Jiménez145 , L. M. Herrmann24 , T. Herrmann50 ,G.Herten
54 , R. Hertenberger109 ,
L. Hervas36 , M. E. Hesping100 , N. P. Hessey156a ,H.Hibi
85 , E. Hill155 , S. J. Hillier20 , J. R. Hinds107 ,
F. Hinterkeuser24 ,M.Hirose
124 ,S.Hirose
157 , D. Hirschbuehl171 , T. G. Hitchings101 ,B.Hiti
93 ,
D. R. Hlaluku33g , J. Hobbs145 , R. Hobincu27e ,N.Hod
169 , M. C. Hodgkinson139 , B. H. Hodkinson32 ,
A. Hoecker36 , D. D. Hofer106 , J. Hofer48 ,T.Holm
24 , M. Holzbock110 , L.B.A.H.Hommels
32 ,
B. P. Honan101 , J. Hong62c , T. M. Hong129 , B. H. Hooberman162 , W. H. Hopkins6,Y.Horii
111 ,S.Hou
148 ,
A. S. Howard93 ,J.Howarth
59 ,J.Hoya
6, M. Hrabovsky122 , A. Hrynevich48 , T. Hryn’ova4,P.J.Hsu
65 ,
S.-C. Hsu138 ,Q.Hu
62a , Y.F.Hu
14a,14e , S. Huang64b , X. Huang14c , X. Huang14a,14e , Y. Huang139 ,
Y. Huang14a , Z. Huang101 , Z. Hubacek132 , M. Huebner24 , F. Huegging24 , T. B. Huffman126 ,C.A.Hugli
48 ,
M. Huhtinen36 , S. K. Huiberts16 , R. Hulsken104 , N. Huseynov12 ,J.Huston
107 ,J.Huth
61 , R. Hyneman143 ,
G. Iacobucci56 , G. Iakovidis29 , I. Ibragimov141 , L. Iconomidou-Fayard66 , P. Iengo72a,72b , R. Iguchi153 ,
T. Ii zawa126 ,Y.Ikegami
84 , N. Ilic155 ,H.Imam
35a , M. Ince Lezki56 , T. Ingebretsen Carlson47a,47b ,
G. Introzzi73a,73b , M. Iodice77a , V. Ippolito75a,75b ,R.K.Irwin
92 ,A.Isaev
37, M. Ishino153 , W. Islam170 ,
C. Issever18,48 , S. Istin21a,al ,H.Ito
168 , J. M. Iturbe Ponce64a , R. Iuppa78a,78b , A. Ivina169 , J.M.Izen
45 ,
V. Izzo72a , P. Jacka131,132 , P. Jackson1, R. M. Jacobs48 , B. P. Jaeger142 , C. S. Jagfeld109 ,G.Jain
156a ,
P. Jai n 54 , K. Jakobs54 , T. Jakoubek169 ,J.Jamieson
59 , K. W. Janas86a , M. Javurkova103 , F. Jeanneau135 ,
L. Jeanty123 , J. Jejelava149a,aa , P. Jenni54,g, C. E. Jessiman34 , S. Jézéquel4,C.Jia
62b,J.Jia
145 ,X.Jia
61 ,
X. Jia14a,14e ,Z.Jia
14c , S. Jiggins48 , J. Jimenez Pena13 ,S.Jin
14c , A. Jinaru27b , O. Jinnouchi154 ,
H. Jivan33g , P. Johansson139 , K. A. Johns7, J. W. Johnson136 , D. M. Jones32 , E. Jones48 , P. Jones32 ,
R. W. L. Jones91 , T. J. Jones92 , H. L. Joos55 ,36 , R. Joshi119 , J. Jovicevic15 ,X.Ju
17a , J. J. Junggeburth103 ,
T. Junkermann63a , A. Juste Rozas13 ,t, M. K. Juzek87 , S. Kabana137e , A. Kaczmarska87 , M. Kado110 ,
H. Kagan119 , M. Kagan143 , A. Kahn41, A. Kahn128 , C. Kahra100 ,T.Kaji
153 , E. Kajomovitz150 , N. Kakati169 ,
I. Kalaitzidou54 , C. W. Kalderon29 , A. Kamenshchikov155 , N.J.Kang
136 ,D.Kar
33g ,K.Karava
126 ,
M. J. Kareem156b , E. Karentzos54 , I. Karkanias152 , O. Karkout114 , S. N. Karpov38 , Z.M.Karpova
38 ,
V. Kartvelishvili91 , A. N. Karyukhin37 ,E.Kasimi
152 , J. Katzy48 , S. Kaur34 , K. Kawade140 ,M.P.Kawale
120 ,
C. Kawamoto88 , T. Kawamoto62a , E.F.Kay
36 , F. I. Kaya158 , S. Kazakos107 , V. F. Kazanin37 ,Y.Ke
145 ,
J. M. Keaveney33a , R. Keeler165 , G. V. Kehris61 , J. S. Keller34 , A. S. Kelly96, J. J. Kempster146 ,
K. E. Kennedy41 , P. D. Kennedy100 , O. Kepka131 , B.P.Kerridge
167 , S. Kersten171 , B. P. Kerševan93 ,
S. Keshri66 , L. Keszeghova28a , S. Ketabchi Haghighat155 ,R.A.Khan
129 , A. Khanov121 ,A.G.Kharlamov
37 ,
T. Kharlamova37 , E. E. Khoda138 , M. Kholodenko37 , T.J.Khoo
18 , G. Khoriauli166 , J. Khubua149b,*,
Y. A. R. Khwaira66 , A. Kilgallon123 ,D.W.Kim
47a,47b ,Y.K.Kim
39 ,N.Kimura
96 , M. K. Kingston55 ,
A. Kirchhoff55 ,C.Kirfel
24 ,F.Kirfel
24 ,J.Kirk
134 , A. E. Kiryunin110 , C. Kitsaki10 ,O.Kivernyk
24 ,
M. Klassen63a , C. Klein34 , L. Klein166 , M.H.Klein
44 , M. Klein92 , S. B. Klein56 , U. Klein92 ,
P. Klimek36 , A. Klimentov29 , T. Klioutchnikova36 , P. Kluit114 , S. Kluth110 , E. Kneringer79 , T. M. Knight155 ,
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A. Knue49 , R. Kobayashi88 , D. Kobylianskii169 , S. F. Koch126 , M. Kocian143 , P. Kodyš133 , D. M. Koeck123 ,
P. T. Koenig24 ,T.Koffas
34 , O. Kolay50 , I. Koletsou4, T. Komarek122 , K. Köneke54 , A.X.Y.Kong
1,
T. Kono118 , N. Konstantinidis96 , P. Kontaxakis56 , B. Konya98 , R. Kopeliansky68 , S. Koperny86a ,
K. Korcyl87 , K. Kordas152,e,A.Korn
96 ,S.Korn
55 , I. Korolkov13 , N. Korotkova37 , B. Kortman114 ,
O. Kortner110 , S. Kortner110 , W. H. Kostecka115 , V. V. Kostyukhin141 , A. Kotsokechagia135 ,A.Kotwal
51 ,
A. Koulouris36 , A. Kourkoumeli-Charalampidi73a,73b , C. Kourkoumelis9, E. Kourlitis110,ae , O. Kovanda146 ,
R. Kowalewski165 , W. Kozanecki135 , A. S. Kozhin37 , V. A. Kramarenko37 , G. Kramberger93 ,P.Kramer
100 ,
M. W. Krasny127 , A. Krasznahorkay36 , J.W.Kraus
171 , J.A.Kremer
48 , T. Kresse50 , J. Kretzschmar92 ,
K. Kreul18 , P. Krieger155 , S. Krishnamurthy103 ,M.Krivos
133 , K. Krizka20 , K. Kroeninger49 , H. Kroha110 ,
J. Kroll131 ,J.Kroll
128 , K.S.Krowpman
107 , U. Kruchonak38 , H. Krüger24 , N. Krumnack81, M. C. Kruse51 ,
O. Kuchinskaia37 , S. Kuday3a , S. Kuehn36 , R. Kuesters54 , T. Kuhl48 , V. Kukhtin38 , Y. Kulchitsky37,a,
S. Kuleshov137b,137d , M. Kumar33g , N. Kumari48 , P. Kumari156b , A. Kupco131 , T. Kupfer49, A. Kupich37 ,
O. Kuprash54 , H. Kurashige85 , L. L. Kurchaninov156a , O. Kurdysh66 , Y. A. Kurochkin37 ,A.Kurova
37 ,
M. Kuze154 ,A.K.Kvam
103 , J. Kvita122 ,T.Kwan
104 , N. G. Kyriacou106 ,L.A.O.Laatu
102 , C. Lacasta163 ,
F. Lacava75a,75b , H. Lacker18 , D. Lacour127 ,N.N.Lad
96 , E. Ladygin38 , B. Laforge127 , T. Lagouri137e ,
F. Z. Lahbabi35a ,S.Lai
55 , I.K.Lakomiec
86a , N. Lalloue60 , D. Lambert40, J. E. Lambert165 , S. Lammers68 ,
W. Lampl7, C. Lampoudis152,e, A. N. Lancaster115 , E. Lançon29 , U. Landgraf54 , M. P. J. Landon94 ,
V. S. Lang54 , R. J. Langenberg103 , O. K. B. Langrekken125 , A. J. Lankford159 , F. Lanni36 , K. Lantzsch24 ,
A. Lanza73a , A. Lapertosa57a,57b , J. F. Laporte135 ,T.Lari
71a , F. Lasagni Manghi23b , M. Lassnig36 ,
V. Latonova131 , A. Laudrain100 , A. Laurier150 , S.D.Lawlor
139 , Z. Lawrence101 , R. Lazaridou167 ,
M. Lazzaroni71a,71b ,B.Le
101, E. M. Le Boulicaut51 , B. Leban93 , A. Lebedev81 , M. LeBlanc101 ,
F. Ledroit-Guillon60 ,A.C.A.Lee
96,S.C.Lee
148 ,S.Lee
47a,47b ,T.F.Lee
92 , L. L. Leeuw33c , H. P. Lefebvre95 ,
M. Lefebvre165 , C. Leggett17a , G. Lehmann Miotto36 , M. Leigh56 , W. A. Leight103 , W. Leinonen113 ,
A. Leisos152,s, M.A.L.Leite
83c , C. E. Leitgeb48 , R. Leitner133 , N. M. Lekalakala33g, K.J.C.Leney
44 ,
T. Lenz24 , S. Leone74a , C. Leonidopoulos52 , A. Leopold144 ,C.Leroy
108 ,R.Les
107 ,C.G.Lester
32 ,
M. Levchenko37 , J. Levêque4,D.Levin
106 , L.J.Levinson
169 , M. P. Lewicki87 , D.J.Lewis
4,A.Li
5,
B. Li62b ,C.Li
62a,C-Q.Li
110 ,H.Li
62a ,H.Li
62b ,H.Li
14c ,H.Li
14b ,H.Li
62b ,J.Li
62c ,K.Li
138 ,
L. Li62c ,M.Li
14a,14e ,Q.Y.Li
62a ,S.Li
14a,14e ,S.Li
62c,62d,d,T.Li
5,X.Li
104 ,Z.Li
126 ,Z.Li
104 ,
Z. Li14a,14e , S. Liang14a,14e , Z. Liang14a ,S.Liao
33g , M. Liberatore135 , B. Liberti76a ,K.Lie
64c ,
J. Lieber Marin83b ,H.Lien
68 ,K.Lin
107 , R. E. Lindley7, J. H. Lindon2, E. Lipeles128 , A. Lipniacka16 ,
A. Lister164 , J. D. Little4,B.Liu
14a ,B.X.Liu
142 ,D.Liu
62c,62d ,J.B.Liu
62a ,J.K.K.Liu
32 ,K.Liu
62c,62d ,
M. Liu62a ,M.Y.Liu
62a ,P.Liu
14a ,Q.Liu
62c,62d,138 ,X.Liu
62a ,X.Liu
62b ,Y.Liu
14d,14e ,Y.L.Liu
62b ,
Y. W. L iu62a , J. Llorente Merino142 , S.L.Lloyd
94 , E. M. Lobodzinska48 , P. Loch7, T. Lohse18 ,
K. Lohwasser139 , E. Loiacono48 , M. Lokajicek131,*, J. D. Lomas20 , J.D.Long
162 , I. Longarini159 ,
L. Longo70a,70b , R. Longo162 , I. Lopez Paz67 , A. Lopez Solis48 , N. Lorenzo Martinez4, A.M.Lory
109 ,
G. Löschcke Centeno146 , O. Loseva37 ,X.Lou
47a,47b ,X.Lou
14a,14e , A. Lounis66 , J. Love6, P.A.Love
91 ,
G. Lu14a,14e ,M.Lu
80 ,S.Lu
128 ,Y.J.Lu
65 , H.J.Lubatti
138 , C. Luci75a,75b , F. L. Lucio Alves14c ,
A. Lucotte60 , F. Luehring68 ,I.Luise
145 , O. Lukianchuk66 , O. Lundberg144 , B. Lund-Jensen144,*,
N. A. Luongo6,M.S.Lutz
151 ,A.B.Lux
25 , D. Lynn29 , H. Lyons92, R. Lysak131 ,E.Lytken
98 ,
V. Lyubushkin38 , T. Lyubushkina38 , M. M. Lyukova145 ,H.Ma
29 ,K.Ma
62a ,L.L.Ma
62b ,W.Ma
62a ,
Y. Ma 121 , D. M. Mac Donell165 , G. Maccarrone53 , J. C. MacDonald100 , P. C. Machado De Abreu Farias83b ,
R. Madar40 , W. F. Mader50 , T. Madula96 , J. Maeda85 , T. Maeno29 , H. Maguire139 , V. Maiboroda135 ,
A. Maio130a,130b,130d ,K.Maj
86a , O. Majersky48 ,S.Majewski
123 , N. Makovec66 , V. Maksimovic15 ,
B. Malaescu127 , Pa. Malecki87 , V. P. Maleev37 , F. Malek60,o,M.Mali
93 , D. Malito95 , U. Mallik80 ,
S. Maltezos10, S. Malyukov38, J. Mamuzic13 , G. Mancini53 , G. Manco73a,73b , J. P. Mandalia94 , I. Mandi´c93 ,
L. Manhaes de Andrade Filho83a , I.M.Maniatis
169 , J. Manjarres Ramos102,ab , D. C. Mankad169 , A. Mann109 ,
B. Mansoulie135 , S. Manzoni36 ,L.Mao
62c , X. Mapekula33c , A. Marantis152,s, G. Marchiori5,
M. Marcisovsky131 , C. Marcon71a , M. Marinescu20 ,S.Marium
48 , M. Marjanovic120 , E. J. Marshall91 ,
Z. Marshall17a , S. Marti-Garcia163 , T. A. Martin167 ,V.J.Martin
52 , B. Martin dit Latour16 , L. Martinelli75a,75b ,
M. Martinez13,t, P. Martinez Agullo163 , V. I. Martinez Outschoorn103 , P. Martinez Suarez13 , S. Martin-Haugh134 ,
V. S. Martoiu27b , A. C. Martyniuk96 , A. Marzin36 , D. Mascione78a,78b , L. Masetti100 , T. Mashimo153 ,
J. Masik101 , A. L. Maslennikov37 , P. Massarotti72a,72b , P. Mastrandrea74a,74b , A. Mastroberardino43a,43b ,
T. Masubuchi153 , T. Mathisen161 , J. Matousek133 , N. Matsuzawa153, J. Maurer27b ,B.Maˇcek93 ,
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D. A. Maximov37 , R. Mazini148 , I. Maznas152 , M. Mazza107 , S. M. Mazza136 , E. Mazzeo71a,71b ,
C. Mc Ginn29 , J.P.McGowan
104 ,S.P.McKee
106 , C. C. McCracken164 , E. F. McDonald105 ,
A. E. McDougall114 , J. A. Mcfayden146 , R. P. McGovern128 , G. Mchedlidze149b , R. P. Mckenzie33g ,
T. C. Mclachlan48 , D. J. Mclaughlin96 , S. J. McMahon134 , C. M. Mcpartland92 , R. A. McPherson165,x,
S. Mehlhase109 , A. Mehta92 , D. Melini150 , B. R. Mellado Garcia33g ,A.H.Melo
55 , F. Meloni48 ,
A. M. Mendes Jacques Da Costa101 , H. Y. Meng155 , L. Meng91 , S. Menke110 , M. Mentink36 , E. Meoni43a,43b ,
G. Mercado115 , C. Merlassino69a ,69c , L. Merola72a,72b , C. Meroni71a ,71b , J. Metcalfe6,A.S.Mete
6,
C. Meyer68 , J-P. Meyer135 , R. P. Middleton134 , L. Mijovi´c52 , G. Mikenberg169 , M. Mikestikova131 ,
M. Mikuž93 , H. Mildner100 , A. Milic36 , C.D.Milke
44 , D. W. Miller39 , E. H. Miller143 , L. S. Miller34 ,
A. Milov169 , D.A.Milstead
47a,47b,T.Min
14c, A. A. Minaenko37 , I. A. Minashvili149b , L. Mince59 ,
A. I. Mincer117 , B. Mindur86a , M. Mineev38 ,Y.Mino
88 , L.M.Mir
13 , M. Miralles Lopez163 ,
M. Mironova17a ,A.Mishima
153, M.C.Missio
113 , A. Mitra167 , V. A. Mitsou163 , Y. Mitsumori111 ,
O. Miu155 , P. S. Miyagawa94 , T. Mkrtchyan63a , M. Mlinarevic96 , T. Mlinarevic96 , M. Mlynarikova36 ,
S. Moayedi8, S. Mobius19 , P. Moder48 , P. Mogg109 , M. H. Mohamed Farook112 , A. F. Mohammed14a,14e ,
S. Mohapatra41 , G. Mokgatitswane33g , L. Moleri169 , B. Mondal141 , S. Mondal132 , K. Mönig48 ,
E. Monnier102 , L. Monsonis Romero163, J. Montejo Berlingen13 , M. Montella119 , F. Montereali77a,77b ,
F. Monticelli90 , S. Monzani69a,69c , N. Morange66 , A. L. Moreira De Carvalho130a , M. Moreno Llácer163 ,
C. Moreno Martinez56 , P. Morettini57b , S. Morgenstern36 ,M.Morii
61 , M. Morinaga153 ,A.K.Morley
36 ,
F. Morodei75a,75b ,L.Morvaj
36 , P. Moschovakos36 , B. Moser36 , M. Mosidze149b , T. Moskalets54 ,
P. Moskvitina113 ,J.Moss
31,l, N. Mosulishvili149c, E.J.W.Moyse
103 , O. Mtintsilana33g , S. Muanza102 ,
J. Mueller129 , D. Muenstermann91 , R. Müller19 , G. A. Mullier161 , A. J. Mullin32, J. J. Mullin128,
D. P. Mungo155 , D. Munoz Perez163 , F. J. Munoz Sanchez101 ,M.Murin
101 , W.J.Murray
167,134 ,
A. Murrone71a,71b , M. Muškinja17a ,C.Mwewa
29 , A. G. Myagkov37,a, A.J.Myers
8, G. Myers68 ,
M. Myska132 , B. P. Nachman17a , O. Nackenhorst49 ,A.Nag
50 , K. Nagai126 , K. Nagano84 ,
J. L. Nagle29,aj , E. Nagy102 ,A.M.Nairz
36 , Y. Nakahama84 , K. Nakamura84 , K. Nakkalil5, H. Nanjo124 ,
R. Narayan44 , E. A. Narayanan112 , I. Naryshkin37 , M. Naseri34 , S. Nasri116b ,C.Nass
24 ,G.Navarro
22a ,
J. Navarro-Gonzalez163 , R. Nayak151 , A. Nayaz18 , P. Y. Nechaeva37 , F. Nechansky48 , L. Nedic126 ,
T. J. Neep20 ,A.Negri
73a,73b ,M.Negrini
23b , C. Nellist114 ,C.Nelson
104 ,K.Nelson
106 , S. Nemecek131 ,
M. Nessi36,h, M. S. Neubauer162 , F. Neuhaus100 , J. Neundorf48 , R. Newhouse164 , P. R. Newman20 ,
C. W. Ng129 , Y.W.Y.Ng
48 ,B.Ngair
116a , H. D. N. Nguyen108 ,R.B.Nickerson
126 , R. Nicolaidou135 ,
J. Nielsen136 , M. Niemeyer55 , J. Niermann55,36 , N. Nikiforou36 , V. Nikolaenko37,a, I. Nikolic-Audit127 ,
K. Nikolopoulos20 , P. Nilsson29 , I. Ninca48 , H.R.Nindhito
56 , G. Ninio151 ,A.Nisati
75a ,N.Nishu
2,
R. Nisius110 , J-E. Nitschke50 , E. K. Nkadimeng33g , T. Nobe153 , D. L. Noel32 , T. Nommensen147 ,
M. B. Norfolk139 , R.R.B.Norisam
96 , B.J.Norman
34 , M. Noury35a ,J.Novak
93 ,T.Novak
48 ,
L. Novotny132 , R. Novotny112 , L. Nozka122 , K. Ntekas159 , N. M. J. Nunes De Moura Junior83b ,
E. Nurse96, J. Ocariz127 , A. Ochi85 , I. Ochoa130a , S. Oerdek48,u, J. T. Offermann39 , R. Oganezov173,
A. Ogrodnik133 ,A.Oh
101 ,C.C.Ohm
144 ,H.Oide
84 ,R.Oishi
153 , M. L. Ojeda48 , Y. Okumura153 ,
L. F. Oleiro Seabra130a , S. A. Olivares Pino137d , D. Oliveira Damazio29 , D. Oliveira Goncalves83a ,
J. L. Oliver159 , Ö. O. Öncel54 , A. P. O’Neill19 , A. Onofre130a,130e , P.U.E.Onyisi
11 , M.J.Oreglia
39 ,
G. E. Orellana90 , D. Orestano77a,77b , N. Orlando13 ,R.S.Orr
155 , V. O’Shea59 , L. M. Osojnak128 ,
R. Ospanov62a , G. Otero y Garzon30 , H. Otono89 ,P.S.Ott
63a , G. J. Ottino17a , M. Ouchrif35d , J. Ouellette29 ,
F. Ould-Saada125 , M. Owen59 , R. E. Owen134 , K. Y. Oyulmaz21a , V. E. Ozcan21a , F. Ozturk87 ,
N. Ozturk8, S. Ozturk82 , H.A.Pacey
126 , A. Pacheco Pages13 , C. Padilla Aranda13 , G. Padovano75a,75b ,
S. Pagan Griso17a , G. Palacino68 , A. Palazzo70a,70b ,J.Pan
172 ,T.Pan
64a , D. K. Panchal11 , C. E. Pandini114 ,
J. G. Panduro Vazquez95 , H. D. Pandya1, H. Pang14b , P. Pani48 , G. Panizzo69a ,69c , L. Paolozzi56 ,
C. Papadatos108 , S. Parajuli162 , A. Paramonov6, C. Paraskevopoulos10 , D. Paredes Hernandez64b ,
K. R. Park41 ,T.H.Park
155 ,M.A.Parker
32 , F. Parodi57a,57b , E.W.Parrish
115 , V. A. Parrish52 ,
J. A. Parsons41 , U. Parzefall54 , B. Pascual Dias108 , L. Pascual Dominguez151 , E. Pasqualucci75a ,
S. Passaggio57b ,F.Pastore
95 , P. Pasuwan47a,47b , P. Patel87 , U.M.Patel
51 , J.R.Pater
101 , T. Pauly36 ,
J. Pearkes143 , M. Pedersen125 , R. Pedro130a , F. M. Pedro Martins130a , S. V. Peleganchuk37 , C. D. Pelwan33g,
O. Penc36 , E. A. Pender52 , K. E. Penski109 , M. Penzin37 , B. S. Peralva83d , A. P. Pereira Peixoto60 ,
L. Pereira Sanchez47a,47b , D. V. Perepelitsa29,aj , E. Perez Codina156a , M. Perganti10 , L. Perini71a ,71b,*,
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G. Piacquadio145 , E. Pianori17a , F. Piazza123 , R. Piegaia30 , D. Pietreanu27b , A. D. Pilkington101 ,
M. Pinamonti69a,69c , J.L.Pinfold
2, B. C. Pinheiro Pereira130a , A. E. Pinto Pinoargote100,135 , L. Pintucci69a,69c ,
K. M. Piper146 , A. Pirttikoski56 , D. A. Pizzi34 , L. Pizzimento64b , A. Pizzini114 , M.-A. Pleier29 ,
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133 , E. Plotnikova38, G. Poddar4, R. Poettgen98 , L. Poggioli127 , I. Pokharel55 ,
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P. Postolache27c , K. Potamianos167 , P. A. Potepa86a , I.N.Potrap
38 , C. J. Potter32 , H. Potti1, T. Poulsen48 ,
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54 ,D.Price
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M. A. Principe Martin99 , R. Privara122 , T. Procter59 , M.L.Profﬁtt
138 , N. Proklova128 , K. Prokoﬁev64c ,
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I. A. Ramirez-Berend34 ,K.Ran
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A. L. Rescia48 , S. Resconi71a , M. Ressegotti57a,57b , S. Rettie36 , J.G.ReyesRivera
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M. Rijssenbeek145 , A. Rimoldi73a ,73b , M. Rimoldi36 , L. Rinaldi23a,23b ,T.T.Rinn
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G. Ripellino161 ,I.Riu
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B. A. Roberts167 , B. R. Roberts17a , S. H. Robertson104,x, D. Robinson32 , C. M. Robles Gajardo137f ,
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A. R. Roepe-Gier136 , J. Roggel171 , O. Røhne125 , R.A.Rojas
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A. Romaniouk37 , E. Romano73a,73b , M. Romano23b , A. C. Romero Hernandez162 , N. Rompotis92 , L. Roos127 ,
S. Rosati75a , B.J.Rosser
39 , E. Rossi126 , E. Rossi72a,72b , L. P. Rossi57b , L. Rossini54 ,R.Rosten
119 ,
M. Rotaru27b , B. Rottler54 , C. Rougier102,ab , D. Rousseau66 , D. Rousso32 ,A.Roy
162 , S. Roy-Garand155 ,
A. Rozanov102 , Z. M. A. Rozario59 , Y. Rozen150 , X. Ruan33g , A. Rubio Jimenez163 , A. J. Ruby92 ,
V. H. Ruelas Rivera18 , T. A. Ruggeri1, A. Ruggiero126 , A. Ruiz-Martinez163 , A. Rummler36 ,Z.Rurikova
54 ,
N. A. Rusakovich38 , H. L. Russell165 , G. Russo75a,75b , J. P. Rutherfoord7, S. Rutherford Colmenares32 ,
K. Rybacki91, M. Rybar133 ,E.B.Rye
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H.F-W. Sadrozinski136 , F. Safai Tehrani75a , B. Safarzadeh Samani134 , M. Safdari143 , S. Saha165 ,
M. Sahinsoy110 , A. Saibel163 , M. Saimpert135 , M. Saito153 , T. Saito153 , D. Salamani36 , A. Salnikov143 ,
J. Salt163 , A. Salvador Salas151 , D. Salvatore43a,43b , F. Salvatore146 , A. Salzburger36 , D. Sammel54 ,
D. Sampsonidis152,e, D. Sampsonidou123 , J. Sánchez163 , A. Sanchez Pineda4, V. Sanchez Sebastian163 ,
H. Sandaker125 , C. O. Sander48 , J. A. Sandesara103 , M. Sandhoff171 , C. Sandoval22b , C. J. Sandrock33g ,
D. P. C. Sankey134 , T. Sano88 , A. Sansoni53 , L. Santi75a ,75b , C. Santoni40 , H. Santos130a,130b ,
A. Santra169 , K. A. Saoucha160 , J. G. Saraiva130a,130d , J. Sardain7, L. Sargsyan173 , O. Sasaki84 ,
K. Sato157 , C. Sauer63b , F. Sauerburger54 , E. Sauvan4,P.Savard
155,ag , R. Sawada153 , C. Sawyer134 ,
L. Sawyer97 , I. Sayago Galvan163, C. Sbarra23b , A. Sbrizzi23a ,23b , T. Scanlon96 , J. Schaarschmidt138 ,
D. Schaefer39 , U. Schäfer100 , A. C. Schaffer66,44 , D. Schaile109 , R. D. Schamberger145 , C. Scharf18 ,
M. M. Schefer19 , V. A. Schegelsky37 , D. Scheirich133 , F. Schenck18 , M. Schernau159 , C. Scheulen55 ,
C. Schiavi57a,57b , E. J. Schioppa70a,70b , M. Schioppa43a,43b , B. Schlag143 ,n, K. E. Schleicher54 , S. Schlenker36 ,
J. Schmeing171 , M. A. Schmidt171 , K. Schmieden100 , C. Schmitt100 , N. Schmitt100 , S. Schmitt48 ,
L. Schoeffel135 , A. Schoening63b , P. G. Scholer54 , E. Schopf126 , M. Schott100 , J. Schovancova36 ,
S. Schramm56 , F. Schroeder171 , T. Schroer56 , H-C. Schultz-Coulon63a , M. Schumacher54 , B. A. Schumm136 ,
Ph. Schune135 , A. J. Schuy138 , H. R. Schwartz136 , A. Schwartzman143 , T. A. Schwarz106 , Ph. Schwemling135 ,
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120 ,F.Sforza
57a,57b ,A.Sfyrla
56 , E. Shabalina55 , R. Shaheen144 ,
J. D. Shahinian128 , D. Shaked Renous169 , N. Shalanda37, A. Shalyugin38, L. Y. Shan14a , M. Shapiro17a ,
A. Sharma36 , A. S. Sharma164 , P. Sharma80 , S. Sharma48 , P. B. Shatalov37 , K. Shaw146 , S.M.Shaw
101 ,
A. Shcherbakova37 , Q. Shen5,62c , D. J. Sheppard142 , P. Sherwood96 , L. Shi96 , X. Shi14a , C. O. Shimmin172 ,
J. D. Shinner95 , I.P.J.Shipsey
126 , S. Shirabe56 ,h, M. Shiyakova38,v, J. Shlomi169 , M. J. Shochet39 ,
J. Shojaii105 , D. R. Shope125 , B. Shrestha120 , S. Shrestha119,ak , E. M. Shrif33g , M. J. Shroff165 , P. Sicho131 ,
A. M. Sickles162 , E. Sideras Haddad33g , A. Sidoti23b , F. Siegert50 , Dj. Sijacki15 , F. Sili90 , J.M.Silva
20 ,
M. V. Silva Oliveira29 , S. B. Silverstein47a , S. Simion66, R. Simoniello36 , E. L. Simpson59 , H. Simpson146 ,
L. R. Simpson106 , N. D. Simpson98,S.Simsek
82 , S. Sindhu55 , P. Sinervo155 , S. Singh155 , S. Sinha48 ,
S. Sinha101 , M. Sioli23a,23b ,I.Siral
36 , E. Sitnikova48 , S.Yu. Sivoklokov37,*, J. Sjölin47a,47b , A. Skaf55 ,
E. Skorda20 , P. Skubic120 , M. Slawinska87 , V. Smakhtin169, B.H.Smart
134 ,J.Smiesko
36 , S.Yu. Smirnov37 ,
Y. Smirnov115,Y.Smirnov
37 ,L.N.Smirnova
37,a,O.Smirnova
98 , A. C. Smith41 , E.A.Smith
39 ,
H. A. Smith126 , J. L. Smith92 , R. Smith143, M. Smizanska91 ,K.Smolek
132 , A. A. Snesarev37 , S. R. Snider155 ,
H. L. Snoek114 , S. Snyder29 , R. Sobie165,x, A. Soffer151 , C. A. Solans Sanchez36 , E.Yu. Soldatov37 ,
U. Soldevila163 , A. A. Solodkov37 , S. Solomon26 , A. Soloshenko38 , K. Solovieva54 , O. V. Solovyanov40 ,
V. Solovyev37 , P. Sommer36 , A. Sonay13 , W. Y. Song156b , J. M. Sonneveld114 , A. Sopczak132 , A. L. Sopio96 ,
F. Sopkova28b , J. D. Sorenson112 , I. R. Sotarriva Alvarez154 , V. Sothilingam63a, O. J. Soto Sandoval137b,137c ,
S. Sottocornola68 , R. Soualah160 , Z. Soumaimi35e , D. South48 , N. Soybelman169 , S. Spagnolo70a,70b ,
M. Spalla110 , D. Sperlich54 , G. Spigo36 , S. Spinali91 , D. P. Spiteri59 , M. Spoor33g, M. Spousta133 ,
E. J. Staats34 , A. Stabile71a ,71b ,R.Stamen
63a , A. Stampekis20 , M. Standke24 , E. Stanecka87 , M. V. Stange50 ,
B. Stanislaus17a , M. M. Stanitzki48 , B. Stapf48 , E. A. Starchenko37 ,G.H.Stark
136 ,J.Stark
102,ab ,
P. Staroba131 , P. Starovoitov63a ,S.Stärz
104 , R. Staszewski87 , G. Stavropoulos46 , J. Steentoft161 ,
P. Steinberg29 , B. Stelzer142,156a , H.J.Stelzer
129 , O. Stelzer-Chilton156a , H. Stenzel58 , T. J. Stevenson146 ,
G. A. Stewart36 ,J.R.Stewart
121 , M. C. Stockton36 , G. Stoicea27b , M. Stolarski130a , S. Stonjek110 ,
A. Straessner50 , J. Strandberg144 , S. Strandberg47a,47b , M. Stratmann171 , M. Strauss120 , T. Strebler102 ,
P. Strizenec28b , R. Ströhmer166 ,D.M.Strom
123 , R. Stroynowski44 , A. Strubig47a,47b , S. A. Stucci29 ,
B. Stugu16 , J. Stupak120 , N.A.Styles
48 ,D.Su
143 ,S.Su
62a ,W.Su
62d ,X.Su
62a,66 , K. Sugizaki153 ,
V. V. Su lin 37 , M.J.Sullivan
92 , D.M.S.Sultan
78a,78b , L. Sultanaliyeva37 , S. Sultansoy3b , T. Sumida88 ,
S. Sun106 , S. Sun170 , O. Sunneborn Gudnadottir161 , N. Sur102 ,M.R.Sutton
146 , H. Suzuki157 ,M.Svatos
131 ,
M. Swiatlowski156a , T. Swirski166 , I. Sykora28a , M. Sykora133 , T. Sykora133 ,D.Ta
100 , K. Tackmann48,u,
A. Taffard159 , R. Taﬁrout156a , J.S.TafoyaVargas
66 , E. P. Takeva52 , Y. Takubo84 , M. Talby102 ,
A. A. Talyshev37 ,K.C.Tam
64b ,N.M.Tamir
151, A. Tanaka153 , J. Tanaka153 , R. Tanaka66 , M. Tanasini57a,57b ,
F. Tang39 ,Z.Tao
164 , S. Tapia Araya137f , S. Tapprogge100 , A. Tarek Abouelfadl Mohamed107 , S. Tarem150 ,
K. Tariq14a , G. Tarna102,27b , G. F. Tartarelli71a ,P.Tas
133 ,M.Tasevsky
131 , E. Tassi43a,43b ,A.C.Tate
162 ,
G. Tateno153 , Y. Tayalati35e,w, G. N. Taylor105 , W. Taylor156b , A.S.Tee
170 , R. Teixeira De Lima143 ,
P. Teixeira-Dias95 , J. J. Teoh155 , K. Terashi153 ,J.Terron
99 , S. Terzo13 ,M.Testa
53 , R. J. Teuscher155,x,
A. Thaler79 , O. Theiner56 , N. Themistokleous52 , T. Theveneaux-Pelzer102 , O. Thielmann171 , D. W. Thomas95,
J. P. Thomas20 , E. A. Thompson17a , P. D. Thompson20 , E. Thomson128 ,Y.Tian
55 , V. Tikhomirov37,a,
Yu.A. Tikhonov37 , S. Timoshenko37,D.Timoshyn
133 , E.X.L.Ting
1, P. Tipton172 , S.H.Tlou
33g ,
A. Tnourji40 , K. Todome154 , S. Todorova-Nova133 , S. Todt50,M.Togawa
84 ,J.Tojo
89 , S. Tokár28a ,
K. Tokushuku84 , O. Toldaiev68 , R. Tombs32 , K.G.Tomiwa
33g , M. Tomoto84,111 , L. Tompkins143,n,
K. W. Topolnicki86b , E. Torrence123 ,H.Torres
102,ab , E. Torró Pastor163 , M. Toscani30 , C. Tosciri39 ,
M. Tost11 , D. R. Tovey139 , A. Traeet16, I. S. Trandaﬁr27b , T. Trefzger166 , A. Tricoli29 , I. M. Trigger156a ,
S. Trincaz-Duvoid127 , D. A. Trischuk26 , B. Trocmé60 , C. Troncon71a , L. Truong33c , M. Trzebinski87 ,
A. Trzupek87 ,F.Tsai
145 ,M.Tsai
106 ,A.Tsiamis
152,e, P. V. Tsiareshka37 , S. Tsigaridas156a , A. Tsirigotis152 ,s,
V. Tsiskaridze155 , E. G. Tskhadadze149a , M. Tsopoulou152,e, Y. Tsujikawa88 ,I.I.Tsukerman
37 ,
V. Tsulaia17a , S. Tsuno84 ,K.Tsuri
118 , D. Tsybychev145 ,Y.Tu
64b , A. Tudorache27b , V. Tudorache27b ,
A. N. Tuna61 , S. Turchikhin57a,57b , I. Turk Cakir3a , R. Turra71a , T. Turtuvshin38,y,P.M.Tuts
41 ,
S. Tzamarias152,e, P. Tzanis10 ,E.Tzovara
100 ,F.Ukegawa
157 , P. A. Ulloa Poblete137b,137c , E. N. Umaka29 ,
G. Unal36 , M. Unal11 , A. Undrus29 , G. Unel159 , J. Urban28b , P. Urquijo105 , P. Urrejola137a ,
G. Usai8, R. Ushioda154 ,M.Usman
108 , Z. Uysal82 , V. Vacek132 , B. Vachon104 , K.O.H.Vadla
125 ,
123

 1313  Page 48 of 53 Eur. Phys. J. C          (2024) 84:1313 
T. Vafeiadis36 , A. Vaitkus96 , C. Valderanis109 , E. Valdes Santurio47a,47b , M. Valente156a , S. Valentinetti23a,23b ,
A. Valero163 , E. Valiente Moreno163 , A. Vallier102,ab , J.A.VallsFerrer
163 , D. R. Van Arneman114 ,
T. R. Van Daalen138 , A. Van Der Graaf49 , P. Van Gemmeren6, M. Van Rijnbach125,36 , S. Van Stroud96 ,
I. Van Vulpen114 , M. Vanadia76a,76b , W. Vandelli36 , M. Vandenbroucke135 , E. R. Vandewall121 ,
D. Vannicola151 , L. Vannoli57a,57b ,R.Vari
75a , E. W. Varnes7, C. Varni17b , T. Varol148 , D. Varouchas66 ,
L. Varriale163 ,K.E.Varvell
147 ,M.E.Vasile
27b , L. Vaslin84, G. A. Vasquez165 , A. Vasyukov38 , F. Vazeille40 ,
T. Vazquez Schroeder36 , J. Veatch31 , V. Vecchio101 , M.J.Veen
103 , I. Veliscek126 , L. M. Veloce155 ,
F. Veloso130a,130c , S. Veneziano75a , A. Ventura70a,70b , S. Ventura Gonzalez135 , A. Verbytskyi110 ,
M. Verducci74a,74b , C. Vergis24 , M. Verissimo De Araujo83b ,W.Verkerke
114 , J. C. Vermeulen114 ,
C. Vernieri143 , M. Vessella103 , M. C. Vetterli142,ag , A. Vgenopoulos152 ,e, N. Viaux Maira137f ,T.Vickey
139 ,
O. E. Vickey Boeriu139 , G. H. A. Viehhauser126 , L. Vigani63b , M. Villa23a,23b , M. Villaplana Perez163 ,
E. M. Villhauer52, E. Vilucchi53 , M. G. Vincter34 , G. S. Virdee20 , A. Vishwakarma52 , A. Visibile114, C. Vittori36 ,
I. Vivarelli146 , E. Voevodina110 , F. Vogel109 ,J.C.Voigt
50 , P. Vokac132 , Yu. Volkotrub86a , J. Von Ahnen48 ,
E. Von Toerne24 , B. Vormwald36 , V. Vorobel133 , K. Vorobev37 ,M.Vos
163 ,K.Voss
141 , J. H. Vossebeld92 ,
M. Vozak114 , L. Vozdecky94 , N. Vranjes15 , M. Vranjes Milosavljevic15 , M. Vreeswijk114 , N.K.Vu
62c,62d ,
R. Vuillermet36 , O. Vujinovic100 , I. Vukotic39 , S. Wada157 , C. Wagner103, J. M. Wagner17a , W. Wagner171 ,
S. Wahdan171 , H. Wahlberg90 , M. Wakida111 , J. Walder134 ,R.Walker
109 , W. Walkowiak141 ,A.Wall
128 ,
T. Wamorkar6, A. Z. Wang136 , C. Wang100 , C. Wang62c , H. Wang17a , J. Wang64a , R.-J. Wang100 ,
R. Wang61 , R. Wang6, S. M. Wang148 , S. Wang62b , T. Wang62a ,W.T.Wang
80 , W. Wang14a , X. Wang14c ,
X. Wang162 , X. Wang62c , Y. Wang62d , Y. Wang14c , Z. Wang106 , Z. Wang51,62c,62d , Z. Wang106 ,
A. Warburton104 ,R.J.Ward
20 , N. Warrack59 ,A.T.Watson
20 ,H.Watson
59 ,M.F.Watson
20 , E. Watton59,134 ,
G. Watts138 , B. M. Waugh96 , C. Weber29 , H. A. Weber18 , M. S. Weber19 , S. M. Weber63a ,C.Wei
62a ,
Y. We i126 , A. R. Weidberg126 , E.J.Weik
117 , J. Weingarten49 , M. Weirich100 ,C.Weiser
54 , C.J.Wells
48 ,
T. Wenaus29 , B. Wendland49 , T. Wengler36 ,N.S.Wenke
110,N.Wermes
24 , M. Wessels63a , A. M. Wharton91 ,
A. S. White61 , A. White8, M.J.White
1, D. Whiteson159 , L. Wickremasinghe124 , W. Wiedenmann170 ,
M. Wielers134 , C. Wiglesworth42 , D. J. Wilbern120, H. G. Wilkens36 , D. M. Williams41 , H. H. Williams128,
S. Williams32 , S. Willocq103 , B.J.Wilson
101 , P. J. Windischhofer39 ,F.I.Winkel
30 , F. Winklmeier123 ,
B. T. Winter54 , J. K. Winter101 , M. Wittgen143, M. Wobisch97 ,Z.Wolffs
114 , J. Wollrath159, M. W. Wolter87 ,
H. Wolters130a,130c , A. F. Wongel48 , E. L. Woodward41 ,S.D.Worm
48 ,B.K.Wosiek
87 , K.W.Wo´zniak87 ,
S. Wozniewski55 , K. Wraight59 ,C.Wu
20 ,J.Wu
14a,14e ,M.Wu
64a ,M.Wu
113 ,S.L.Wu
170 ,X.Wu
56 ,
Y. Wu62a ,Z.Wu
135 , J. Wuerzinger110,ae , T. R. Wyatt101 , B. M. Wynne52 , S. Xella42 ,L.Xia
14c ,M.Xia
14b ,
J. Xiang64c ,M.Xie
62a ,X.Xie
62a ,S.Xin
14a,14e , A. Xiong123 , J. Xiong17a ,D.Xu
14a ,H.Xu
62a ,L.Xu
62a ,
R. Xu128 ,T.Xu
106 ,Y.Xu
14b ,Z.Xu
52 ,Z.Xu
14c, B. Yabsley147 , S. Yacoob33a , Y. Yamaguchi154 ,
E. Yamashita153 , H. Yamauchi157 , T. Yamazaki17a , Y. Yamazaki85 ,J.Yan
62c,S.Yan
126 ,Z.Yan
25 ,
H. J. Yang62c,62d , H. T. Yang62a , S. Yang62a , T. Yang64c , X. Yang36 , X. Yang14a , Y. Yang44 , Y. Yang62a,
Z. Yang62a ,W-M.Yao
17a ,Y.C.Yap
48 ,H.Ye
14c ,H.Ye
55 ,J.Ye
14a ,S.Ye
29 ,X.Ye
62a ,Y.Yeh
96 ,
I. Yeletskikh38 ,B.K.Yeo
17b , M.R.Yexley
96 ,P.Yin
41 , K. Yorita168 , S. Younas27b , C. J. S. Young36 ,
C. Young143 ,C.Yu
14a,14e,ai ,Y.Yu
62a , M. Yuan106 , R. Yuan62b ,L.Yue
96 , M. Zaazoua62a , B. Zabinski87 ,
E. Zaid52,Z.K.Zak
87 , T. Zakareishvili149b , N. Zakharchuk34 , S. Zambito56 , J. A. Zamora Saa137b,137d ,
J. Zang153 , D. Zanzi54 , O. Zaplatilek132 , C. Zeitnitz171 , H. Zeng14a , J.C.Zeng
162 , D. T. Zenger Jr26 ,
O. Zenin37 , T. Ženiš28a , S. Zenz94 , S. Zerradi35a ,D.Zerwas
66 , M. Zhai14a,14e , D. F. Zhang139 , J. Zhang62b ,
J. Zhang6, K. Zhang14a ,14e , L. Zhang14c , P. Zhang14a,14e , R. Zhang170 , S. Zhang106 , S. Zhang44 ,
T. Zhang153 , X. Zhang62c , X. Zhang62b , Y. Zhang5,62c , Y. Zhang96 , Y. Zhang14c , Z. Zhang17a , Z. Zhang66 ,
H. Zhao138 , T. Zhao62b , Y. Zhao136 , Z. Zhao62a , A. Zhemchugov38 , J. Zheng14c , K. Zheng162 , X. Zheng62a ,
Z. Zheng143 , D. Zhong162 , B. Zhou106 , H. Zhou7, N. Zhou62c , Y. Zhou14c , Y. Zhou7,C.G.Zhu
62b ,
J. Zhu106 ,Y.Zhu
62c ,Y.Zhu
62a , X. Zhuang14a , K. Zhukov37 , V. Zhulanov37 ,N.I.Zimine
38 , J. Zinsser63b ,
M. Ziolkowski141 ,L.Živkovi´c15 , A. Zoccoli23a,23b , K. Zoch61 , T. G. Zorbas139 ,O.Zormpa
46 ,W.Zou
41 ,
L. Zwalinski36
1Department of Physics, University of Adelaide, Adelaide, Australia
2Department of Physics, University of Alberta, Edmonton, AB, Canada
3(a)Department of Physics, Ankara University, Ankara, Türkiye; (b)Division of Physics, TOBB University of Economics
and Technology, Ankara, Türkiye
123

Eur. Phys. J. C (2024) 84:1313 Page 49 of 53 1313

4LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France

5APC, Université Paris Cité, CNRS/IN2P3, Paris, France

6High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA

7Department of Physics, University of Arizona, Tucson, AZ, USA

8Department of Physics, University of Texas at Arlington, Arlington, TX, USA

9Physics Department, National and Kapodistrian University of Athens, Athens, Greece

10 Physics Department, National Technical University of Athens, Zografou, Greece

11 Department of Physics, University of Texas at Austin, Austin, TX, USA

12 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan

13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain

14 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China; (b)Physics Department, Tsinghua

University, Beijing, China; (c)Department of Physics, Nanjing University, Nanjing, China; (d)School of Science,

Shenzhen Campus of Sun Yat-sen University, Shenzhen, China; (e)University of Chinese Academy of Science (UCAS),

Beijing, China

15 Institute of Physics, University of Belgrade, Belgrade, Serbia

16 Department for Physics and Technology, University of Bergen, Bergen, Norway

17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (b)University of California, Berkeley,

CA, USA

18 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany

19 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern,

Switzerland

20 School of Physics and Astronomy, University of Birmingham, Birmingham, UK

21 (a)Department of Physics, Bogazici University, Istanbul, Türkiye; (b)Department of Physics Engineering, Gaziantep

University, Gaziantep, Türkiye; (c)Department of Physics, Istanbul University, Istanbul, Türkiye

22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá, Colombia; (b)Departamento

de Física, Universidad Nacional de Colombia, Bogotá, Colombia

23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna, Italy; (b)INFN Sezione di Bologna,

Bologna, Italy

24 Physikalisches Institut, Universität Bonn, Bonn, Germany

25 Department of Physics, Boston University, Boston, MA, USA

26 Department of Physics, Brandeis University, Waltham, MA, USA

27 (a)Transilvania University of Brasov, Brasov, Romania; (b)Horia Hulubei National Institute of Physics and Nuclear

Engineering, Bucharest, Romania; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi,

Romania; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics

Department, Cluj-Napoca, Romania; (e)National University of Science and Technology Politechnica, Bucharest,

Romania; (f)West University in Timisoara, Timisoara, Romania; (g)Faculty of Physics, University of Bucharest,

Bucharest, Romania

28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic; (b)Department of

Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic

29 Physics Department, Brookhaven National Laboratory, Upton, NY, USA

30 Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y CONICET, Instituto

de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina

31 California State University, CA, USA

32 Cavendish Laboratory, University of Cambridge, Cambridge, UK

33 (a)Department of Physics, University of Cape Town, Cape Town, South Africa; (b)iThemba Labs, Western Cape, South

Africa; (c)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg,

South Africa; (d)National Institute of Physics, University of the Philippines Diliman, Quezon City,

Philippines; (e)University of South Africa, Department of Physics, Pretoria,

South Africa; (f)University of Zululand, KwaDlangezwa, South Africa; (g)School of Physics, University of the

Witwatersrand, Johannesburg, South Africa

34 Department of Physics, Carleton University, Ottawa, ON, Canada

35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - Université Hassan II,

Casablanca, Morocco; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra, Morocco; (c)Faculté des Sciences

123

1313 Page 50 of 53 Eur. Phys. J. C (2024) 84:1313

Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco; (d)LPMR, Faculté des Sciences, Université Mohamed

Premier, Oujda, Morocco; (e)Faculté des sciences, Université Mohammed V, Rabat, Morocco; (f)Institute of Applied

Physics, Mohammed VI Polytechnic University, Ben Guerir, Morocco

36 CERN, Geneva, Switzerland

37 Afﬁliated with an institute covered by a cooperation agreement with CERN, Geneva, Switzerland

38 Afﬁliated with an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland

39 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA

40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France

41 Nevis Laboratory, Columbia University, Irvington, NY, USA

42 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

43 (a)Dipartimento di Fisica, Università della Calabria, Rende, Italy; (b)INFN Gruppo Collegato di Cosenza, Laboratori

Nazionali di Frascati, Italy

44 Physics Department, Southern Methodist University, Dallas, TX, USA

45 Physics Department, University of Texas at Dallas, Richardson, TX, USA

46 National Centre for Scientiﬁc Research “Demokritos”, Agia Paraskevi, Greece

47 (a)Department of Physics, Stockholm University, Sweden; (b)Oskar Klein Centre, Stockholm, Sweden

48 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany

49 Fakultät Physik , Technische Universität Dortmund, Dortmund, Germany

50 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany

51 Department of Physics, Duke University, Durham, NC, USA

52 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK

53 INFN e Laboratori Nazionali di Frascati, Frascati, Italy

54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany

55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany

56 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland

57 (a)Dipartimento di Fisica, Università di Genova, Genova, Italy; (b)INFN Sezione di Genova, Genova, Italy

58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany

59 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow, UK

60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France

61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA

62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science

and Technology of China, Hefei, China; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of

Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; (c)School of Physics and

Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC,

Shanghai, China; (d)Tsung-Dao Lee Institute, Shanghai, China; (e)School of Physics and Microelectronics, Zhengzhou

University, Zhengzhou, China

63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; (b)Physikalisches Institut,

Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

64 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T, Hong Kong, China; (b)Department of Physics,

University of Hong Kong, Hong Kong, China; (c)Department of Physics and Institute for Advanced Study, Hong Kong

University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

65 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan

66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay, France

67 Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona, Spain

68 Department of Physics, Indiana University, Bloomington, IN, USA

69 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; (b)ICTP, Trieste, Italy; (c)Dipartimento Politecnico

di Ingegneria e Architettura, Università di Udine, Udine, Italy

70 (a)INFN Sezione di Lecce, Lecce, Italy; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy

71 (a)INFN Sezione di Milano, Milan, Italy; (b)Dipartimento di Fisica, Università di Milano, Milan, Italy

72 (a)INFN Sezione di Napoli, Naples, Italy; (b)Dipartimento di Fisica, Università di Napoli, Naples, Italy

73 (a)INFN Sezione di Pavia, Pavia, Italy; (b)Dipartimento di Fisica, Università di Pavia, Pavia, Italy

74 (a)INFN Sezione di Pisa, Pisa, Italy; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy

75 (a)INFN Sezione di Roma, Rome, Italy; (b)Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy

123

Eur. Phys. J. C (2024) 84:1313 Page 51 of 53 1313

76 (a)INFN Sezione di Roma Tor Vergata, Rome, Italy; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma,

Italy

77 (a)INFN Sezione di Roma Tre, Rome, Italy; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy

78 (a)INFN-TIFPA, Povo, Italy; (b)Università degli Studi di Trento, Trento, Italy

79 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck, Austria

80 University of Iowa, Iowa City, IA, USA

81 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA

82 Istinye University, Sariyer, Istanbul, Türkiye

83 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora,

Brazil; (b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil; (c)Instituto de Física,

Universidade de São Paulo, São Paulo, Brazil; (d)Rio de Janeiro State University, Rio de Janeiro, Brazil

84 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan

85 Graduate School of Science, Kobe University, Kobe, Japan

86 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow, Poland; (b)Marian

Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland

87 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland

88 Faculty of Science, Kyoto University, Kyoto, Japan

89 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka , Japan

90 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina

91 Physics Department, Lancaster University, Lancaster, UK

92 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK

93 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana,

Ljubljana, Slovenia

94 School of Physics and Astronomy, Queen Mary University of London, London, UK

95 Department of Physics, Royal Holloway University of London, Egham, UK

96 Department of Physics and Astronomy, University College London, London, UK

97 Louisiana Tech University, Ruston, LA, USA

98 Fysiska institutionen, Lunds universitet, Lund, Sweden

99 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain

100 Institut für Physik, Universität Mainz, Mainz, Germany

101 School of Physics and Astronomy, University of Manchester, Manchester, UK

102 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France

103 Department of Physics, University of Massachusetts, Amherst, MA, USA

104 Department of Physics, McGill University, Montreal, QC, Canada

105 School of Physics, University of Melbourne, Victoria, Australia

106 Department of Physics, University of Michigan, Ann Arbor, MI, USA

107 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA

108 Group of Particle Physics, University of Montreal, Montreal, QC, Canada

109 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany

110 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany

111 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan

112 Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA

113 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen, Netherlands

114 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands

115 Department of Physics, Northern Illinois University, DeKalb, IL, USA

116 (a)New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; (b)United Arab Emirates University, Al Ain,

United Arab Emirates

117 Department of Physics, New York University, New York, NY, USA

118 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan

119 Ohio State University, Columbus, OH, USA

120 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA

121 Department of Physics, Oklahoma State University, Stillwater, OK, USA

122 Palacký University, Joint Laboratory of Optics, Olomouc, Czech Republic

123

1313 Page 52 of 53 Eur. Phys. J. C (2024) 84:1313

123 Institute for Fundamental Science, University of Oregon, Eugene, OR, USA

124 Graduate School of Science, Osaka University, Osaka, Japan

125 Department of Physics, University of Oslo, Oslo, Norway

126 Department of Physics, Oxford University, Oxford, UK

127 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France

128 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA

129 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA

130 (a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa, Portugal; (b)Departamento de Física,

Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal; (c)Departamento de Física, Universidade de Coimbra,

Coimbra, Portugal; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa, Portugal; (e)Departamento de Física,

Universidade do Minho, Braga, Portugal; (f)Departamento de Física Teórica y del Cosmos, Universidad de Granada,

Granada, Spain; (g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

131 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic

132 Czech Technical University in Prague, Prague, Czech Republic

133 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic

134 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK

135 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

136 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA

137 (a)Departamento de Física, Pontiﬁcia Universidad Católica de Chile, Santiago, Chile; (b)Millennium Institute for

Subatomic physics at high energy frontier (SAPHIR), Santiago, Chile; (c)Instituto de Investigación Multidisciplinario en

Ciencia y Tecnología, y Departamento de Física, Universidad de La Serena, Chile; (d)Universidad Andres Bello,

Department of Physics, Santiago, Chile; (e)Instituto de Alta Investigación, Universidad de Tarapacá, Arica,

Chile; (f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile

138 Department of Physics, University of Washington, Seattle, WA, USA

139 Department of Physics and Astronomy, University of Shefﬁeld, Shefﬁeld, UK

140 Department of Physics, Shinshu University, Nagano, Japan

141 Department Physik, Universität Siegen, Siegen, Germany

142 Department of Physics, Simon Fraser University, Burnaby, BC, Canada

143 SLAC National Accelerator Laboratory, Stanford, CA, USA

144 Department of Physics, Royal Institute of Technology, Stockholm, Sweden

145 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA

146 Department of Physics and Astronomy, University of Sussex, Brighton, UK

147 School of Physics, University of Sydney, Sydney, Australia

148 Institute of Physics, Academia Sinica, Taipei, Taiwan

149 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b)High Energy

Physics Institute, Tbilisi State University, Tbilisi, Georgia; (c)University of Georgia, Tbilisi, Georgia

150 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel

151 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

152 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

153 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan

154 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan

155 Department of Physics, University of Toronto, Toronto, ON, Canada

156 (a)TRIUMF, Vancouver, BC, Canada; (b)Department of Physics and Astronomy, York University, Toronto, ON, Canada

157 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences,

University of Tsukuba, Tsukuba, Japan

158 Department of Physics and Astronomy, Tufts University, Medford, MA, USA

159 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA

160 University of Sharjah, Sharjah, United Arab Emirates

161 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden

162 Department of Physics, University of Illinois, Urbana, IL, USA

163 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain

164 Department of Physics, University of British Columbia, Vancouver, BC, Canada

165 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada

123

Eur. Phys. J. C (2024) 84:1313 Page 53 of 53 1313

166 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany

167 Department of Physics, University of Warwick, Coventry, UK

168 Waseda University, Tokyo, Japan

169 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot, Israel

170 Department of Physics, University of Wisconsin, Madison, WI, USA

171 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal,

Germany

172 Department of Physics, Yale University, New Haven, CT, USA

173 Yerevan Physics Institute, Yerevan, Armenia

aAlso Afﬁliated with an institute covered by a cooperation agreement with cern, Geneva, Switzerland

bAlso at An-najah national university, Nablus, Palestine

cAlso at Borough of Manhattan Community College, City University of New York, New York, NY, USA

dAlso at Center for High Energy Physics, Peking University, Beijing, China

eAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece

fAlso at Centro Studi e Ricerche Enrico Fermi, Rome, Italy

gAlso at CERN, Geneva, Switzerland

hAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerland

iAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain

jAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece

kAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva, Israel

lAlso at Department of Physics, California State University, Sacramento, USA

mAlso at Department of Physics, King’s College London, London, UK

nAlso at Department of Physics, Stanford University, Stanford, CA, USA

oAlso at Department of Physics, Stellenbosch University, Stellenbosch, South Africa

pAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland

qAlso at Department of Physics, University of Thessaly, Volos, Greece

rAlso at Department of Physics, Westmont College, Santa Barbara, USA

sAlso at Hellenic Open University, Patras, Greece

tAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain

uAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany

vAlso at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences,

Soﬁa, Bulgaria

wAlso at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir, Morocco

xAlso at Institute of Particle Physics (IPP), Toronto, Canada

yAlso at Institute of Physics and Technology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia

zAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan

aa Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia

ab Also at L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse, France

ac Also at Lawrence Livermore National Laboratory, Livermore, USA

ad Also at National Institute of Physics, University of the Philippines Diliman (Philippines), Philippines

ae Also at Technical University of Munich, Munich, Germany

af Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China

ag Also at TRIUMF, Vancouver, BC, Canada

ah Also at Università di Napoli Parthenope, Napoli, Italy

ai Also at University of Chinese Academy of Sciences (UCAS), Beijing, China

aj Also at University of Colorado Boulder, Department of Physics, Colorado, USA

ak Also at Washington College, Chestertown, MD, USA

al Also at Yeditepe University, Physics Department, Istanbul, Türkiye

∗Deceased

123

Study of the radiation hardness of the ATLAS Tile Calorimeter optical instrumentation with Run 2 data

Article

Jun 2025
J INSTRUM

This paper presents a study of the radiation hardness of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2. Both the plastic scintillators constituting the detector active media and the wavelength-shifting optical fibres collecting the scintillation light into the photodetector readout are elements susceptible to radiation damage. The dedicated calibration and monitoring systems of the detector (caesium radioactive sources, laser and minimum bias integrator) allow to assess the response of these optical components. Data collected with these systems between 2015 and 2018 are analysed to measure the degradation of the optical instrumentation across Run 2. Moreover, a simulation of the total ionising dose in the calorimeter is employed to study and model the degradation profile as a function of the exposure conditions, both integrated dose and dose rate. The measurement of the relative light output loss in Run 2 is presented and extrapolations to future scenarios are drawn based on current data. The impact of radiation damage on the cell response uniformity is also analysed.

Versal Adaptive Compute Acceleration Platform Processing for ATLAS-TileCal Signal Reconstruction

Article

May 2025

Particle detectors at accelerators generate large amounts of data, requiring analysis to derive insights. Collisions lead to signal pile-up, where multiple particles produce signals in the same detector sensors, complicating individual signal identification. This contribution describes the implementation of a deep-learning algorithm on a Versal Adaptive Compute Acceleration Platform (ACAP) device for improved processing via parallelization and concurrency. Connected to a host computer via Peripheral Component Interconnect express (PCIe), this system aims for enhanced speed and energy efficiency over Central Processing Units (CPUs) and Graphics Processing Units (GPUs). In the contribution, we will describe in detail the data processing and the hardware, firmware and software components of the system. The contribution presents the implementation of the deep-learning algorithm on a Versal ACAP device, as well as the system for transferring data in an efficient way.

Laser calibration of the ATLAS Tile Calorimeter during LHC Run 2

Article

Full-text available

Jun 2023
J INSTRUM

This article reports the laser calibration of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2 data campaign. The upgraded Laser II calibration system is described. The system was commissioned during the first LHC Long Shutdown, exhibiting a stability better than 0.8% for the laser light monitoring. The methods employed to derive the detector calibration factors with data from the laser calibration runs are also detailed. These allowed to correct for the response fluctuations of the 9852 photomultiplier tubes of the Tile Calorimeter with a total uncertainty of 0.5% plus a luminosity-dependent sub-dominant term. Finally, we report the regular monitoring and performance studies using laser events in both standalone runs and during proton collisions. These studies include channel timing and quality inspection, and photomultiplier linearity and response dependence on anode current.

Radiation hardness of the ATLAS Tile Calorimeter optical components

Article

Full-text available

Dec 2020

B Pereira

The Tile Calorimeter (TileCal) is a sampling hadronic calorimeter and an essential part of the ATLAS experiment at the LHC. Plastic scintillating tiles are the active material. The light produced in the scintillators is transmitted to the photomultiplier tubes by wavelength shifting fibres. During the High Luminosity LHC (HL-LHC) programme, the luminosity can reach a value several times higher than the one that TileCal was designed for. Two critical points that affect the detector performance are the increased exposure to radiation that does degrade the TileCal optics and natural ageing. Since the optical components of the TileCal cannot be replaced, the radiation hardness must be evaluated. The Laser and Cesium calibration systems are used to evaluate the robustness of the TileCal optical components. These systems combined allow to isolate the response of the tiles and fibres and evaluate the evolution of the light yield with the dose. Run 2 calibration data were analysed, indicating that cells in layer A, and B11 and C10 cells have lost about 5% of light yield. No significant changes were found for the other cells. This study constitutes an essential step for predicting the calorimeter performance in future HL-LHC runs. Nevertheless, the extrapolation uncertainty is large so more data needs to be explored to reach better precision on such extrapolation.

Operation of the ATLAS trigger system in Run 2

Article

Full-text available

Oct 2020
J INSTRUM

ATLAS Collaboration

The ATLAS experiment at the Large Hadron Collider employs a two-level trigger system to record data at an average rate of 1 kHz from physics collisions, starting from an initial bunch crossing rate of 40 MHz. During the LHC Run 2 (2015-2018), the ATLAS trigger system operated successfully with excellent performance and flexibility by adapting to the various run conditions encountered and has been vital for the ATLAS Run-2 physics programme. For proton-proton running, approximately 1500 individual event selections were included in a trigger menu which specified the physics signatures and selection algorithms used for the data-taking, and the allocated event rate and bandwidth. The trigger menu must reflect the physics goals for a given data collection period, taking into account the instantaneous luminosity of the LHC and limitations from the ATLAS detector readout, online processing farm, and offline storage. This document discusses the operation of the ATLAS trigger system during the nominal proton-proton data collection in Run 2 with examples of special data-taking runs. Aspects of software validation, evolution of the trigger selection algorithms during Run 2, monitoring of the trigger system and data quality as well as trigger configuration are presented.

The Cesium source calibration and monitoring system of the ATLAS Tile Calorimeter: design, construction and results

Article

Full-text available

Mar 2020
J INSTRUM

Event generation with Sherpa 2.2

Article

Full-text available

Sep 2019

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarise essential features and improvements of the Sherpa 2.2 release series, which is heavily used for event generation in the analysis and interpretation of LHC Run 1 and Run 2 data. We highlight a decade of developments towards ever higher precision in the simulation of particle-collision events.

Optics robustness of the ATLAS Tile Calorimeter

Article

Full-text available

May 2019

Rhona Pedro

TileCal, the central hadronic calorimeter of the ATLAS detector is composed of plastic scintillators interleaved by steel plates, and wavelength shifting optical fibres. The optical properties of these components are known to suffer from natural ageing and degrade due to exposure to radiation. The calorimeter was designed for 10 years of LHC operating at the design luminosity of

10^{34}

^{-2}

^{-1}

. Irradiation tests of scintillators and fibres have shown that their light yield decrease by about 10% for the maximum dose expected after 10 years of LHC operation. The robustness of the TileCal optics components is evaluated using the calibration systems of the calorimeter: Cs-137 gamma source, laser light, and integrated photomultiplier signals of particles from proton-proton collisions. It is observed that the loss of light yield increases with exposure to radiation as expected. The decrease in the light yield during the years 2015-2017 corresponding to the LHC Run 2 will be reported. The current LHC operation plan foresees a second high luminosity LHC (HL-LHC) phase extending the experiment lifetime for 10 years more. The results obtained in Run 2 indicate that following the light yield response of TileCal is an essential step for predicting the calorimeter performance in future runs. Preliminary studies attempt to extrapolate these measurements to the HL-LHC running conditions.

Performance of missing transverse momentum reconstruction with the ATLAS detector using proton-proton collisions at

\sqrt{s}

= 13 TeV

Article

Full-text available

Feb 2018

ATLAS Collaboration

The performance of the missing transverse momentum (E

_{T}^{miss}

) reconstruction with the ATLAS detector is evaluated using data collected in proton-proton collisions at the LHC at a center-of-mass energy of 13 TeV in 2015. To reconstruct E

_{T}^{miss}

, fully calibrated electrons, muons, photons, hadronically decaying

\tau

-leptons, and jets reconstructed from calorimeter energy deposits and charged-particle tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-particle tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-particle tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various E

_{T}^{miss}

contributions. The individual terms as well as the overall reconstructed E

_{T}^{miss}

are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the E

_{T}^{miss}

scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3.2 fb

^{-1}

A matched filter based approach for high-energy estimation in calorimetry

Article

Feb 2021
J INSTRUM

Calibration and Performance of the ATLAS Tile Calorimeter During the Run 2 of the LHC: Volume 2

Chapter

Aug 2018

Oleg Solovyanov

The Tile Calorimeter (TileCal) is a hadronic calorimeter covering the central region of the ATLAS experiment at the LHC. It is a non-compensating sampling calorimeter comprised of steel and scintillating plastic tiles which are read-out by photomultiplier tubes (PMT). The TileCal is regularly monitored and calibrated by several different calibration systems: a Cs radioactive source that illuminates the scintillating tiles directly, a laser light system to directly test the PMT response, and a charge injection system (CIS) for the front-end electronics.

The High Voltage distribution system of the ATLAS Tile Calorimeter and its performance during data taking

Article

Apr 2018
J INSTRUM

This article documents the characteristics of the high voltage (HV) system of the hadronic calorimeter TileCal of the ATLAS experiment. Such a system is suitable to supply reliable power distribution into particles physics detectors using a large number of PhotoMultiplier Tubes (PMTs). Measurements performed during the 2015 and 2016 data taking periods of the ATLAS detector show that its performance, in terms of stability and noise, fits the specifications. In particular, almost all the PMTs show a voltage instability smaller than 0.5 V corresponding to a gain stability better than 0.5%. A small amount of channels was found not working correctly. To diagnose the origin of such defects, the results of the HV measurements were compared to those obtained using a Laser system. The analysis shows that less than 0.2% of the about 10 thousand HV channels were malfunctioning.

Operation and performance of the ATLAS tile calorimeter in LHC Run 2

Abstract

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