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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

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The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Comparison of the average CPU time per event in the standard pile-up (SPU) digitisation (black open circles) and the presampled pile-up (PSPU) digitisation (red filled circles) as a function of the number of pp\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document} collisions per bunch crossing (μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mu $$\end{document}). The CPU time is normalised to the time taken for the standard pile-up for the lowest μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mu $$\end{document} bin. For this measurement, tt¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t\bar{t}$$\end{document} events are used for the hard-scatter event. The average is taken over 1000 events and the vertical error bars represent the standard deviation of the separate CPU time measurements. For the standard pile-up digitisation, the slope of the relative CPU time per event versus μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document} is 0.108\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.108$$\end{document}, while for the presampled pile-up digitisation, it is 0.002\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.002$$\end{document}
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Comput Softw Big Sci (2022) 6:3
https://doi.org/10.1007/s41781-021-00062-2
Original Article
Emulating the impact of additional proton–proton interactions in
the ATLAS simulation by presampling sets of inelastic Monte
Carlo events
ATLAS Collaboration
CERN, 1211 Geneva 23, Switzerland
Received: 8 March 2021 / Accepted: 19 July 2021
© The Author(s) 2022
Abstract The accurate simulation of additional interac-
tions at the ATLAS experiment for the analysis of proton–
proton collisions delivered by the Large Hadron Collider
presents a significant challenge to the computing resources.
During the LHC Run 2 (2015–2018), there were up to 70
inelastic interactions per bunch crossing, which need to be
accounted for in Monte Carlo (MC) production. In this doc-
ument, a new method to account for these additional interac-
tions in the simulation chain is described. Instead of sampling
the inelastic interactions and adding their energy deposits to a
hard-scatter interaction one-by-one, the inelastic interactions
are presampled, independent of the hard scatter, and stored as
combined events. Consequently, for each hard-scatter inter-
action, only one such presampled event needs to be added
as part of the simulation chain. For the Run 2 simulation
chain, with an average of 35 interactions per bunch cross-
ing, this new method provides a substantial reduction in MC
production CPU needs of around 20%, while reproducing the
properties of the reconstructed quantities relevant for physics
analyses with good accuracy.
Contents
1 Introduction ..................... 1
2 ATLAS detector ................... 3
3 Overview of simulation chain ............ 4
4 Computing performance comparison ........ 5
5 Inner detector ..................... 8
5.1 Detector readout ................. 8
5.1.1 Silicon pixel detector ........... 8
5.1.2 Silicon microstrip detector (SCT) .... 9
5.1.3 Transition radiation tracker (TRT) .... 9
5.2 Overlay procedure ................ 9
5.2.1 Pixel detector ............... 9
5.2.2 SCT detector ............... 9
e-mail: atlas.publications@cern.ch
5.2.3 TRT detector ............... 10
5.3 Validation results ................ 10
6 Calorimeters ..................... 12
6.1 Detector readout ................. 12
6.2 Overlay procedure ................ 13
6.3 Validation results ................ 14
7 Muon spectrometer .................. 14
7.1 Detector readout and overlay procedure .... 14
7.1.1 Monitored drift tubes (MDT) ....... 14
7.1.2 Cathode strip chambers (CSC) ...... 14
7.1.3 Resistive plate chambers (RPC) ..... 14
7.1.4 Thin-gap chambers (TGC) ........ 15
7.2 Validation results ................ 16
8 Trigger ........................ 17
8.1 L1 calorimeter trigger simulation ........ 18
8.2 HLT simulation and performance ........ 20
9 Conclusions ..................... 20
References ........................ 21
1 Introduction
The excellent performance of the Large Hadron Collider
(LHC) creates a challenging environment for the ATLAS
and CMS experiments. In addition to the hard-scatter proton–
proton ( pp) interaction which is of interest for a given physics
analysis, a large number of inelastic proton–proton collisions
occur simultaneously. These are collectively known as pile-
up. The mean number of these inelastic pp interactions per
bunch crossing, μ, also known as the pile-up parameter, char-
acterises the instantaneous luminosity at any given time1.
For physics analyses, pile-up is conceptually similar to
a noise contribution that needs to be accounted for as it is
unrelated to the hard-scatter event that is of interest for the
analysis. Since nearly all analyses rely on Monte Carlo (MC)
1Hereafter, the approximation that μis the same for all colliding bunch
pairsismade.
0123456789().: V,-vol 123
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3 Page 2 of 35 Comput Softw Big Sci (2022) 6:3
Fig. 1 The μdistribution observed for the ATLAS Run 2 data, for each
year (2015–2018) separately and for the sum of all years [4]
simulation to predict the detector response to the physics pro-
cess, it is crucial that the pile-up is modelled correctly as part
of that simulation. The goal of the ATLAS MC simulation
chain is to accurately reproduce the pile-up, such that it can
be accounted for in physics analyses.
Within ATLAS, the pile-up is emulated by overlaying soft
inelastic pp interactions, in the following called minimum-
bias interactions, generated with an MC generator, normally
Pythia [1], according to the pile-up profile for a given data-
taking period. Figure 1shows the μdistribution for each
year during Run 2 (2015–2018) and the sum of all years. The
mean value is 34.2, but the distribution is broad and gen-
erally covers values between 10 and 70. The small peak at
μ2 arises from special running periods with rather low
luminosity. At the High Luminosity LHC (HL-LHC), μis
expected to increase to about 200 [2]. The inelastic inter-
actions include non-diffractive and diffractive interactions
based on the Donnachie–Landshoff [3] model for the cross
sections of the individual processes.
The simulation chain for MC events contains several steps,
starting from the generation of the interactions with an MC
generator (e.g., Pythia,Sherpa [5]). The interactions of the
generated particles with the ATLAS detector are simulated
using a Geant4-based [6] simulation framework [7]. This
is performed separately for the hard-scatter interactions of
interest and a large number of minimum-bias interactions.
Next, the readout of the detector is emulated via a process
known as digitisation, which takes into account both the hard-
scatter and any overlapping minimum-bias interactions. In
this article, two methods of performing the digitisation are
compared. The goal of the new method, described below,
is to reduce the computing resources required by creating a
large set of pile-up events only once for an MC production
campaign and then reusing these events for different hard-
scatter events. A similar method has been explored by the
CMS collaboration [8].
In the first method, referred to as standard pile-up here-
after, the hard-scatter interaction and the desired number of
minimum-bias interactions are read in simultaneously during
the digitisation step and the energy deposits made by particles
are added for each detector element. Then, the detector read-
out is emulated to convert these into digital signals, which
are finally used in the event reconstruction. This method cre-
ates the pile-up on demand for each hard-scatter event, and
has been used up to now for all ATLAS publications based
on pp collisions. In the second (and new) method, referred
to as presampled pile-up hereafter, this same procedure is
followed but for the set of minimum-bias interactions alone,
without the hard-scatter interaction. The resulting presam-
pled events are written out and stored. Then, during the digiti-
sation of a given hard-scatter interaction, a single presampled
event is picked and its signal added to that of the hard-scatter
interaction for each readout channel. This combined event
is then input to the event reconstruction. In contrast to the
first method, the same presampled pile-up event can be used
for several hard-scatter interactions. For both methods, the μ
value to be used is sampled randomly from the data μdistri-
bution, such that the ensemble of many events follows the μ
distribution of the data.
If the detector signals were read out without any infor-
mation loss, the two methods would give identical results.
However, in reality, some information loss occurs due to read-
out thresholds applied or custom compression algorithms
designed to reduce the data volume. This can lead to differ-
ences in the reconstructed quantities used in physics analyses.
While in most cases for ATLAS, these differences were found
to be negligible, in some cases, corrections were derived to
reduce the impact on physics analyses, as is discussed in
Sects. 58.
Within the ATLAS Collaboration, a significant validation
effort took place to ensure that this presampled pile-up sim-
ulation chain reproduces the results from the standard pile-
up simulation chain accurately, so that there is no impact
on physics analyses whether one or the other is used. To
this end, thousands of distributions were compared between
the presampled and standard pile-up simulation chains. In
this article, a representative subset of relevant distributions
is shown. Only comparisons between the two methods are
shown in this article; detailed comparisons of data with sim-
ulation can be found in various performance papers; see, e.g.,
Refs. [914].
The motivation for using the presampled pile-up simula-
tion chain in the future is that it uses significantly less CPU
time than the standard pile-up simulation chain. As is dis-
cussed in Ref. [15], savings in CPU, memory, and disk space
requirements are pivotal for the future running of the ATLAS
experiment. Additionally, the presampled pile-up simulation
chain can also be seen as a step towards using minimum-bias
data, instead of presampled simulated events, for emulating
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Comput Softw Big Sci (2022) 6:3 Page 3 of 35 3
the pile-up, which could potentially improve the accuracy of
the modelling of the pile-up interactions. However, the pile-
up emulation with data is not yet validated and not the subject
of this article.
The article is organised as follows. A description of the
ATLAS detector is given in Sect. 2, highlighting the aspects
that are most relevant for the pile-up emulation. Section 3
describes both the standard and presampled pile-up simu-
lation chain, and Sect. 4compares their CPU and memory
performances. In Sects. 58, the challenges in the inner detec-
tor, calorimeters, muon system, and trigger are described
and comparisons of the impact of the old and new meth-
ods are shown. For these comparisons, a variety of different
MC samples are used, based on what is most appropriate for
the validation of a given object within the particular detector
subsystem.
For all studies presented in this article, unless other-
wise stated, the distribution of the average number of events
per bunch crossing follows the distribution observed in the
ATLAS data in 2017, with an average μvalue of 37.8(see
Fig. 1). The ATLAS detector configuration corresponds to
that of Run 2. As the detector configuration evolves in the
future, the new presampled pile-up method will need to be
validated for those new detector elements.
2 ATLAS detector
The ATLAS detector [16] at the LHC covers nearly the
entire solid angle around the collision point. It consists of
an inner tracking detector surrounded by a thin supercon-
ducting solenoid, electromagnetic and hadronic calorimeters,
and a muon spectrometer incorporating three large supercon-
ducting toroidal magnets. A two-level trigger system is used
to select interesting events [17]. The first-level (L1) trigger
is implemented in hardware and uses a subset of detector
information to reduce the event rate from 40 MHz to 100
kHz. This is followed by a software-based high-level trigger
(HLT) which reduces the event rate to an average of 1 kHz.
At the LHC, typically 2400 bunches from each of the two
proton beams cross each other at the ATLAS interaction point
per beam revolution, with one bunch crossing (BC) taking
place every 25ns. In each BC, several pp interactions may
occur. Whenever an L1 trigger signal is received for a given
BC, the entire detector is read out and processed in the HLT
to decide whether the event is stored for further analysis.
The inner detector (ID) is immersed ina2Taxialmag-
netic field and provides charged-particle tracking in the pseu-
dorapidity2range |η|<2.5. The high-granularity silicon
2ATLAS 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
pixel detector (Pixel), including an insertable B-layer (IBL)
[18,19] added in 2014 as a new innermost layer, covers the
vertex region and typically provides four measurements per
track; the first hit normally being in the innermost layer. It is
followed by the silicon microstrip tracker (SCT) which usu-
ally provides four two-dimensional measurement points per
track. These silicon detectors are complemented by a straw
tracker (transition radiation tracker, TRT), which enables
radially extended track reconstruction with an average of
30 hits per track up to |η|=2.0. Additionally, the transi-
tion radiation capability provides separation power between
electrons and charged pions.
The calorimeter system covers the pseudorapidity range
|η|<4.9. Within the region |η|<3.2, electromagnetic
(EM) calorimetry is provided by barrel (EMB) and endcap
(EMEC) high-granularity lead/liquid-argon (LAr) electro-
magnetic calorimeters, with an additional thin LAr presam-
pler covering |η|<1.8 to correct for energy loss in mate-
rial upstream of the calorimeters. Hadronic calorimetry is
provided by the steel/scintillator-tile (Tile) calorimeter, seg-
mented into three barrel structures within |η|<1.7, and
two copper/LAr hadronic endcap calorimeters (HEC). The
solid angle coverage is completed with forward copper/LAr
and tungsten/LAr calorimeter (FCAL) modules optimised for
electromagnetic and hadronic measurements, respectively.
The muon spectrometer (MS) comprises separate trigger
and high-precision tracking chambers measuring the deflec-
tion of muons in a toroidal magnetic field generated by the
superconducting air-core magnets. The field integral of the
toroids ranges between 2.0 and 6.0 T across most of the detec-
tor. A set of precision chambers covers the region |η|<2.7
with three stations of monitored drift tubes (MDTs), com-
plemented by cathode strip chambers (CSCs) in the forward
region, where the background is highest. The muon trigger
system covers the range |η|<2.4 with resistive plate cham-
bers (RPCs) in the barrel, and thin-gap chambers (TGCs) in
the endcap regions.
The integration times of the different subdetectors vary
significantly, mostly due to the charge drift times depending
on the material and geometry of the respective detector sys-
tem. In most cases, the integration time exceeds 25 ns, i.e., the
time between two BCs. In such cases, the signal from events
that occurred in previous BCs contaminates the signal in the
triggered BC. This is often referred to as out-of-time pile-up
and needs to be considered for the simulation, in addition
to the in-time pile-up which accounts for signals generated
the LHC ring, and the y-axis points upwards. Cylindrical coordinates
(r) 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 /2). Angular distance is measured in units of
R(η)2+(φ)2.
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3 Page 4 of 35 Comput Softw Big Sci (2022) 6:3
by interactions occurring inside the BC corresponding to the
hard-scatter event.
Figure 2shows the readout windows considered for the
simulation of each of the detector systems. The MDTs have
the longest integration time, 750 ns, with 32 BCs prior to
the trigger and 6 BCs after the trigger being considered. For
the LAr calorimeter, it is only slightly shorter. For the inner
detector (Pixel, SCT, and TRT), the integration time is much
shorter, and only the 1–2 BCs before and after the trigger
need to be considered.
3 Overview of simulation chain
As is described above, the ATLAS simulation chain [7], used
to produce MC samples to be used in physics and perfor-
mance studies, is divided into three steps: generation of the
event and immediate decays, particle tracking and physics
interactions in the detector, based on Geant4 (G4), and digi-
tisation of the energy deposited in the sensitive regions of
the detector into voltages and currents to emulate the readout
of the ATLAS detector. This simulation chain is integrated
into the ATLAS software framework, Athena [20]. Finally,
a series of reconstruction algorithms is applied in the same
way as for the data, where final physics objects such as jets,
muons, and electrons are reconstructed [16]. Each step can
be run as an individual task, but to save disk space, the digi-
tisation step is usually performed in the same task as the
reconstruction step, such that the intermediate output format
from the digitisation step only needs to be stored locally on
the computing node and can be discarded after the recon-
struction step is finished.
The G4 simulation step is run by itself and, since it is
independent of the detector readout configuration, the trig-
ger, and the pile-up, it is often run significantly earlier than
the digitisation and reconstruction, which depend on these
aspects. The G4 simulation is the most CPU intensive, and
thus, it is desirable to run this as rarely as possible.
The ATLAS digitisation software converts the energy
deposits (HITS) produced by the G4 simulation in the sensi-
tive elements into detector response objects, known as digits.
A digit is produced when the voltage or current of a partic-
ular readout channel rises above a preconfigured threshold
within a particular time window. Some of the subdetectors
read out just the triggered BC, while others read out sev-
eral bunch crossings, creating digits for each. For each digit,
some subdetectors (e.g., SCT) record only the fact that a
given threshold has been exceeded, while others (e.g., Pixel
or LAr) also retain information related to the amplitude. The
digits of each subdetector are written out as Raw Data Objects
(RDOs), which contain information about the readout chan-
nel identifier and the raw data that are sent from the detector
front-end electronics.
For any given hard-scatter interaction, the additional pile-
up interactions must be included in a realistic model of the
detector response. For this purpose, minimum-bias events
are generated using the Pythia event generator with the
NNPDF2.3LO [21] parton distribution function and the A3
[22] set of tuned parameters, and then simulated and stored in
separate files. To avoid potential issues due to low statistics
of relatively hard minimum-bias events, containing objects
with high transverse momentum ( pT), the sampled events are
in fact split into two distinct equal-sized samples: a high-pT
sample composed of events containing jets or photons with
pT>35 GeV and a low-pTsample composed of the remain-
ing events. Events are then selected from each sample based
on their relative cross-section to avoid duplicating distinctive
hard events, which may give rise to visible features during
analysis.
In the current standard pile-up simulation chain, the sim-
ulation files of both the hard-scatter event and the desired
number of minimum-bias events are read in concurrently at
the digitisation step and the HITS are combined. For each
hard-scatter event, a value of μis assigned by randomly sam-
pling the μdistribution corresponding to the relevant data-
taking period. Most subdetector responses are affected by
interactions from neighbouring bunch crossings: as is shown
in Fig. 2, up to 32 BCs before and 6 BCs after the triggering
BC may contribute signal to the trigger BC. For the average
μvalue of 37.8 during 2017 data taking, this implies that
simulating the impact of pile-up on any given hard-scatter
event requires approximately (32 +1+6)×38 =1482
minimum-bias events on average to be selected at random
(from the simulated event files) and processed as part of the
digitisation step. Each of these bunch crossings is taken to
have the same value of μas the trigger bunch crossing3.
The number of minimum-bias events (N) to include for each
bunch crossing is drawn at random from a Poisson distribu-
tion with a mean of the μvalue for that bunch crossing. After
the energy deposits in the trigger BC due to all contributing
BCs have been combined, the detector response is emulated.
This workflow is illustrated in Fig. 3.
The new presampled pile-up simulation chain is illustrated
in Fig. 4. Rather than digitising the minimum-bias inter-
actions each time a hard-scatter event is produced, a large
sample of pile-up events is produced by pre-combining the
simulated pile-up interactions, according to the μdistribu-
tion of the data campaign, during a separate digitisation step,
termed presampling4. Here, the sampling is done exactly as
for the standard pile-up, the only difference being that there
3In data, there are variations between adjacent bunches of order 10%
[23], but this is not emulated in the MC simulation.
4For the calorimeters and the SCT, the digitised output stored in the
presampled events is amended, so that the presampled pile-up can be
applied accurately.
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Comput Softw Big Sci (2022) 6:3 Page 5 of 35 3
Fig. 2 The time windows
considered for the simulation of
each subdetector. The dark blue
BCs are those where a signal in
that BC can contaminate the
signal in the triggered BC (i.e.,
BC 0), while the light
blue-coloured BCs cannot affect
the triggered BC
Fig. 3 Current workflow diagram from simulation to physics analysis. The oval steps represent an action, while the boxes represent data files of a
given format. The final box is the reconstructed data in analysis format
is no hard-scatter event. These presampled pile-up events are
written out in RDO format as pile-up RDO datasets and typ-
ically contain several million events. Each simulated hard-
scatter interaction is then digitised and combined with an
event sampled from these pile-up datasets (step 3 in Fig. 4,
called overlay). Here, instead of HITS for each channel, the
signals of the RDO or digit (depending on the subdetector)
in the hard-scatter event and the presampled event are over-
laid. To avoid double-counting, instrumental noise associated
with the detector electronics is included solely in the presam-
pled RDOs and not included in the subsequent hard-scatter
digitisation. Since the digitisation, presampling, and recon-
struction steps are typically combined into a single task in
the production workflow, the output is written locally to an
RDO file that is then input to the reconstruction software; this
local RDO file is subsequently discarded. The pile-up RDO
datasets necessary for a given digitisation task are about five
times smaller than the many minimum-bias HITS required
in the standard pile-up simulation chain.
The main benefit of the presampled pile-up simulation
chain is that the CPU and I/O requirements of the digitisa-
tion are significantly lower and have a much smaller depen-
dence on μ, as is discussed in Sect. 4. However, if a threshold
or compression has been applied to the signal when writ-
ing the RDO/digit, this results in some loss of information
and thereby could reduce the accuracy of the simulation
when using the presampled pile-up method, as is discussed
in Sects. 58.
For all the comparisons shown in these sections, the hard-
scatter events are identical for the two methods, but the pile-
up events are different. This makes the estimation of the
uncertainties difficult as the hard-scatter is fully correlated,
while the pile-up is not. As most of the quantities are selected
to be sensitive to pile-up, the uncertainties are calculated
assuming the two samples are uncorrelated, but in some dis-
tributions, this leads to an overestimate of the uncertainties,
e.g., in the reconstruction efficiencies of tracks and leptons
and in the trigger efficiencies.
4 Computing performance comparison
In this section, the performances of the two simulation chains
are compared in terms of CPU time, memory usage, and I/O.
The validation in terms of physics performance is presented
in subsequent sections.
The main computing performance benefit of the presam-
pled pile-up simulation chain stems from the fact a pile-up
dataset is only created once per MC production campaign,
and then, the individual events within that dataset are used for
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3 Page 6 of 35 Comput Softw Big Sci (2022) 6:3
Fig. 4 The presampled pile-up workflow schema. The oval steps represent an action, while the boxes represent data files of a given format. The
final box is the reconstructed data in analysis format
multiple hard-scatter MC samples, as opposed to being cre-
ated on demand independently for each MC sample. An MC
production campaign happens typically once per data-taking
period and comprises billions (B) of hard-scatter events and
thousands of individual samples. A sample is defined as a
set of MC events generated using the same input param-
eters, e.g., a sample of t¯
tevents produced by a certain MC
generator with a given set of input parameters. The same pre-
sampled pile-up event can thus be overlaid on many different
hard-scatter events from different MC samples. In doing so,
care needs to be taken to ensure that no undesirable effects
on physics analyses occur due to reusing the same pile-up
events, as is discussed below.
In ATLAS, typically 70% of the CPU resources are
devoted to MC production via the simulation chain; the
remainder is used for data processing and user analyses. At
present, with the Run 2 pile-up profile, the simulation chain
CPU usage is broken down into about 15% for event gen-
eration, 45% for G4 simulation, 20% for digitisation, and
20% for other tasks (reconstruction, trigger, and event writ-
ing). The presampled pile-up scheme decreases the digitisa-
tion time to a negligible level by reusing the pile-up events
more efficiently and thus reduces the overall CPU resources
required for MC production by about 20%, as is discussed
below.
The average CPU time per event in the standard and pre-
sampled pile-up simulation chains as a function of μis shown
in Fig. 5. As can be seen, both depend linearly on μ,but
the slope is about 50 times larger for the standard pile-up
than for the presampled pile-up simulation chain. For the
standard pile-up simulation chain, the CPU time required at
μ=70 is 7.5 times larger than for μ=10, while for the pre-
sampled pile-up method, the corresponding increase in CPU
time is only a factor of 1.2. Extrapolating this to μ=200,
the CPU time is 20 times greater than for μ=10 for the
standard method and <2 times higher for the presampled
pile-up method. However, this comparison does not account
for the CPU time required for the production of the presam-
pled pileup dataset, which is needed to assess the overall CPU
benefit in a realistic campaign, as is discussed below.
10 20 30 40 50 60
μ
0
1
2
3
4
5
6
7
8
Relative CPU time per event
standard (SPU)
presampled (PSPU)
ATLAS Simulation
Fig. 5 Comparison of the average CPU time per event in the standard
pile-up (SPU) digitisation (black open circles) and the presampled pile-
up (PSPU) digitisation (red filled circles) as a function of the number
of pp collisions per bunch crossing (μ). The CPU time is normalised
to the time taken for the standard pile-up for the lowest μbin. For
this measurement, t¯
tevents are used for the hard-scatter event. The
average is taken over 1000 events and the vertical error bars represent
the standard deviation of the separate CPU time measurements. For
the standard pile-up digitisation, the slope of the relative CPU time per
event versus μis 0.108, while for the presampled pile-up digitisation,
it is 0.002
Figure 6shows the memory used by the various steps as
a function of time for the different production steps for the
two simulation chains. The time estimate is based on run-
ning 2000 hard-scatter events for the 2017 μdistribution
on the same CPU in all cases, so that the three scenarios
can be directly compared. The absolute number, of course,
depends on the CPU used and the μdistribution. The pre-
sampling takes about 7 s per event. The standard digitisation
takes about 8 s per event, while the hard-scatter digitisa-
tion and overlay of the presampled pile-up take about 0.5 s.
The remaining steps, which are the same for the two simula-
123
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Comput Softw Big Sci (2022) 6:3 Page 7 of 35 3
tion chains, take about 8 s and include the trigger emulation,
reconstruction, and the writing of the analysis format to disk.
When comparing the required CPU time between the two
chains, the following equations provide a good approxima-
tion. For the standard pile-up simulation chain, the time
Tstandard required is simply given by the number of events
in the campaign times the total time tdigi +tother, where tother
is the sum of the times needed for reconstruction, trigger, and
writing the event to disk. Thus
Tstandard =NMC-campaign ×(tdigi +tother),
where NMC-campaign is the number of hard-scatter events pro-
duced in a given MC-campaign.
For the presampled pile-up simulation chain, the time
Tpresample required is given by the number of events in the
campaign times the time needed for the overlay step and other
aspects plus the time required for the presampling. This last
contribution is given by the total number of presampled pile-
up events required (Npp) multiplied by the event digitisation
time, so that the required time is
Tpresample =NMC-campaign ×(toverlay +tother)+Npp ×tdigi.
The time reduction factor of the presampled pile-up sim-
ulation chain compared to the standard is then given by
Tpresample
Tstandard =NMC-campaign ×(toverlay +tother)+Npp ×tdigi
NMC-campaign ×(tother +tdigi)
1
tother +tdigi tother +tdigi ×Npp
NMC-campaign ,
where the approximation toverlay tother is made, based on
the observations from Fig. 6.
It is immediately clear that the presampled pile-up sim-
ulation chain uses less CPU time than the standard pile-up
simulation chain, since Npp <NMC-campaign . Choosing the
exact value for Npp, however, is not trivial. In general, the
reuse of a given presampled pile-up event within a particular
MC sample, representing an individual hard-scatter physics
process, should be avoided if possible; otherwise, each over-
laid hard-scatter plus pile-up event would not be statistically
independent. Such oversampling would be particularly wor-
risome if the presampled pile-up event in question contained
a distinctive feature, such as a high-transverse-momentum
jet, which could cause difficulties in using the MC sample
for the statistical interpretation of the data distributions. In
practice, such a repetition would not be statistically signifi-
cant in the bulk of a distribution, but could be problematic in
the tails, where there are few events. Given this, it is reason-
able that the value for Npp be chosen to be about the size of
the largest individual MC sample, so that no event is repeated
within it.
For the ATLAS Run 2 MC-campaign, NMC-campaign
10 B and the single largest individual MC sample had a size
of 0.2 B events. Allowing for some increase in these sizes to
be commensurate with the size of the evolving data samples,
Npp 0.5 B should thus be sufficient. Taking the resulting
NMC-campaign/Npp 20, along with tother tdigi (as seen in
Fig. 6), the ratio of the times required for the two methods
is Tpresample/Tstandard 0.53. Hence, the presampled pile-up
simulation chain provides a CPU saving of 47% for the com-
bined digitisation, reconstruction, trigger, and writing steps,
compared to the standard pile-up simulation chain. If the time
required for reconstruction and trigger is further improved (as
is planned for Run 3), or the digitisation time were to fur-
ther increase due to pile-up, the ratio would decrease; e.g.,
if tother tdigi/2, a CPU saving of 63% would be realised.
The 50% reduction translates to an overall MC produc-
tion CPU saving of around 20%, since 60% of the CPU time
required for the simulation chain is at present used for event
generation and G4 simulation, which is not affected by this
improvement. These are illustrative examples that confirm
the intuitive expectation that performing the digitisation just
once per campaign is much more effective than doing it for
each simulated hard-scatter event, as the number of presam-
pled events needed is by construction smaller than the number
of hard-scatter events.
From the memory usage point of view, the presampled
pile-up load is similar to the standard pile-up and well below
the (soft) production limit of 2 GB per core (see Fig. 6)for
the μvalues observed during Run 2 and expected for Run 3.
However, compared to the standard pile-up, the presampled
pile-up simulation chain puts less stress on the I/O system
both because, as is mentioned above, the presampled pile-up
dataset files are about a factor of five smaller and because
they can be read sequentially. The sequential reading is pos-
sible, because the random access necessary to combine the
minimum-bias input files in the standard pile-up is now per-
formed only once at the presampling stage. Hence, the pre-
sampled pile-up RDO production, with its heavier require-
ments, can be performed on a limited subset of ATLAS MC
production sites designed to cope well with such workloads;
the subsequent presampled pile-up simulation chain will then
run on all resources available to ATLAS, utilising the 20%
of sites that have previously been excluded for reconstruc-
tion due to insufficient I/O or disk resources. The reduced
input size also enables the usage of opportunistic resources
such as high-performance computing (HPC) sites, which typ-
ically have less available storage to ATLAS. The smaller I/O
requirements from the presampled pile-up simulation chain
jobs simplify the production workflow, and make it possible
to transfer the pile-up datasets on demand to the computing
node at a given production site, where they are needed. If net-
work speed is further increased in the future, it might even
be possible to access them directly via the network during
the job from a remote storage site.
123
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3 Page 8 of 35 Comput Softw Big Sci (2022) 6:3
Fig. 6 The memory usage
profile of different production
steps as a function of the job
wall-time for 2000 hard-scatter
events. The presampling (top),
the standard pile-up (middle)
and the presampled pile-up
(bottom) simulation chain are
compared. In the latter case, “HS
digi.” refers to the digitisation of
the hard-scatter event. The
underlying μdistribution is that
corresponding to the 2017 data
μdistribution
0
0.5
1
1.5
Memory per CPU Core [GB
]
digitisation
writing
ATLAS Simulation
pile-up presampling
standard pile-up chain
presampled pile-up chain
0
0.5
1
1.5
digitisation
writing
trigger reco.
writing
0 5000 10000 15000 20000 25000 30000
Time [s]
0
0.5
1
1.5
HS digi. + overlay
writing
trigger reco.
writing
The Analysis Object Data (AOD) event size written to disk
is the same for both methods, i.e., there is neither advantage
nor disadvantage in using the presampled pile-up simulation
chain in this regard. However, the many simulated minimum-
bias events do not have to be distributed as widely any more
throughout the year as they only need to be accessed once
for creating the presampled events. These presampled events
need to be made available widely though. It is expected that
these two effects roughly cancel out, but operational experi-
ence is needed to understand how to distribute the presampled
sample in the most effective way.
5 Inner detector
The ID consists of three subdetectors which all use different
technologies as discussed in Sect. 2. Each of them has sep-
arate digitisation software and hence a different treatment
for the presampled pile-up procedure is required for each.
In this section, the readout of the three ID subdetectors is
described, along with the presampled pile-up procedure for
each. Validation results are also presented.
5.1 Detector readout
5.1.1 Silicon pixel detector
The charge produced by a particle traversing a silicon pixel
is integrated if it passes a set threshold. In Run 2, this thresh-
old is typically around 2500 electrons for the IBL and 3500
electrons for the remainder of the Pixel detector. The result-
ing charge deposited by a minimum-ionising particle (MIP)
that traverses a single pixel is typically 16,000 and 20,000
electrons, respectively. The amount of charge deposited by
a particle traversing the detector varies depending on the
path length of the particle through the active silicon and can
be spread across multiple pixels. The length of time dur-
ing which the charge signal exceeds the threshold, termed
time-over-threshold (ToT), is recorded. The ToT is roughly
proportional to the charge. While most of the charge drifts
to the pixel readout within the 25 ns bunch crossing time of
the LHC, there is a small fraction which may take longer and
only arrive in the subsequent bunch crossing (BC+1). Thus,
in any given bunch crossing, the pile-up events both from the
previous and the current bunch crossings contribute hits.
123
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Comput Softw Big Sci (2022) 6:3 Page 9 of 35 3
5.1.2 Silicon microstrip detector (SCT)
For the SCT, the readout is in principle similar to the Pixel
detector in that a threshold is applied for each strip. However,
in contrast to the pixel readout, it is purely digital, i.e., neither
the charge nor the ToT is stored for a given strip, just a bit, X
= 0 or 1, to signal a hit (1) or the absence of a hit (0). Hence,
the hit from the current BC as well as that of the two adjacent
bunch crossings (i.e. BC–1 and BC+1) are read out. Several
data compression modes have been used since the first LHC
collisions; they are defined by the hit pattern of the three time
bins:
Any-hit mode (1XX, X1X or XX1); channels with a sig-
nal above threshold in either the current, previous, or next
bunch crossing are read out.
Level mode (X1X); only channels with a signal above
threshold in the current bunch crossing are read out.
Edge mode (01X); only channels with a signal above
threshold in the current bunch crossing and explicitly no
hit in the preceding bunch crossing are read out.
The data can be compressed further by storing, for adjacent
strips with hits above threshold, only the address of the first
strip and the number of these adjacent strips. When this com-
pression is invoked, the information about which of the three
bunch crossings observed a hit for a given strip is lost. When
the LHC is running with 25 ns bunch spacing, SCT RDOs
are required to satisfy the 01X hit pattern to be considered
during event reconstruction to suppress pile-up from the pre-
vious crossings.
5.1.3 Transition radiation tracker (TRT)
When a particle crosses one of the tubes in the TRT, the elec-
trons drift to the anode wire, producing an electrical signal. If
the charge of that signal exceeds a low discriminator thresh-
old, a corresponding hit is recorded, in eight time slices of
3.125 ns each. The drift time is calculated based on the time
of the first hit, which is subsequently converted to distance to
give a drift-circle radius. In addition, to provide information
for electron identification, a record is kept of whether a high
discriminator threshold is exceeded in any of the eight time
slices. This information is stored for the previous, current,
and subsequent bunch crossings (i.e., BC–1, BC, BC+1).
5.2 Overlay procedure
The quantities which are overlaid for the inner detector are
the RDOs. Due to the high number of channels in the inner
detector, zero suppression5is employed to reduce the amount
of data read out and stored from the detector. Since for the
ID, the RDOs do not contain the full information of the HITS
created by simulation, the overlay of RDO information is less
accurate than the overlay of the underlying HITS informa-
tion. However, the impact on physics observables is gener-
ally found to be negligible as is described in the following;
where a difference is observed, a parameterised correction is
derived as is described below.
5.2.1 Pixel detector
The pixel detector has in excess of 90 M readout channels
and a very high granularity. The single-pixel occupancy is
below 2.5×105per unit μin all layers [24], so even at
μ100, it is below 0.25%. Therefore, the chance that a
single pixel which contains a signal due to a charged par-
ticle from the hard-scatter event also contains one from the
overlapping in-time pile-up events is <0.25%. A pixel RDO
contains the channel identifier and a 32-bit packed word con-
taining the ToT, a bunch-crossing identifier, and information
related to the L1 trigger not relevant in simulation. In the
presampled pile-up, if an RDO of a given channel contains a
hit above threshold from either the hard-scatter event or the
pile-up event, but not both, the corresponding RDO is kept
and written out. In the 0.25% of cases where it contains
a hit above threshold in both the hard-scatter event and the
pile-up event, only the hard-scatter RDO is kept to retain the
ToT (and thus, for example, the energy deposited per path
length dE/dx) from the signal process. This causes a small
loss of information as in principle the ToT would be mod-
ified by the presence of the additional charge deposited in
that pixel from the pile-up events. However, as it only affects
a small fraction of cases, it has a negligible impact on the
overall physics performance. In addition, there could be a
loss of information if, for a given pixel, both the hard-scatter
event and the pile-up event produce charge deposits which
are below the readout threshold but whose sum is above the
threshold. In this case, the presampled pile-up method will
register no hit, while the standard method will register a hit
above threshold. This effect could reduce the cluster size
and the ToT. But again, only a very small fraction of pixels
are affected, so both the cluster size and the ToT agree well
between the two methods.
5.2.2 SCT detector
The SCT is a strip detector with 6.3 M readout channels and
an occupancy in high pile-up conditions of O(1%); conse-
quently, the pile-up modelling is more critical than for the
5Rather than reading out all channels, only those channels containing
data (above a certain significance level) are recorded.
123
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3 Page 10 of 35 Comput Softw Big Sci (2022) 6:3
pixel detector. To facilitate accurate modelling, it is impor-
tant that presampled RDOs be stored in any-hit mode, with-
out further compression, to ensure that the impact of out-of-
time pile-up is modelled correctly. To combine hard-scatter
and pile-up RDOs, all of the strips that are hit on a module
are unpacked from the respective RDOs and repacked into
RDOs using the desired compression mode. Loss of infor-
mation only occurs if hits in both the hard-scatter event and
the pile-up event are below threshold, but the sum of the two
charges is above threshold. In this case, in the standard digi-
tisation a hit would be present, while with the presampled
pile-up procedure, it is not, causing the presampled pile-up
procedure potentially to result in fewer SCT hits per track.
The impact is, however, negligible as is shown below.
5.2.3 TRT detector
The TRT is a straw tube detector with 320 k readout channels,
and in high pile-up conditions, the occupancy of the TRT
exceeds 10%. Therefore, pile-up has a major impact on the
TRT signals. If the channel identifiers in the hard-scatter and
pile-up events are the same, the data word stored is set to a bit-
wise logical OR of the corresponding raw words. This results
in some loss of information as the sum of the charge signals
will be larger, and thus more easily pass a given threshold,
than would be just the sum of the digitised signals. This
particularly impacts the fraction of hits that pass the high
discriminator threshold.
A correction for this effect is applied to improve the level
of agreement between the presampled pile-up and the stan-
dard digitisation. For this correction, a high-threshold (HT)
bit is activated according to a randomised procedure, tuned
to describe the standard digitisation. The rate of randomly
activating a high-threshold bit is parameterised as a linear
function of the occupancy of the TRT in the simulated pile-up
events (a proxy for the average energy deposited in the pile-
up events) and whether the charged particle that is traversing
the straw from the hard-scatter event is an electron or not. A
different correction is applied for electrons as they produce
significant amounts of transition radiation in the momentum
range relevant for physics analysis (5–140 GeV), while all
other particles do not. The correction corresponds to approx-
imately a 10% (5%) increase in the number of HT hits for
electrons (non-electrons) at the average Run 2 μvalue.
5.3 Validation results
To validate the presampled pile-up digitisation for each of the
subdetectors, the properties of tracks in simulated t¯
tevents,
where at least one Wboson from the top quarks decays
leptonically, are compared between the presampled pile-up
method and the standard digitisation. The t¯
tevents are cho-
sen, because they represent a busy detector environment and
contain tracks from a wide range of physics objects.
The primary track reconstruction is performed using an
iterative track-finding procedure seeded from combinations
of silicon detector measurements. The track candidates must
have a transverse momentum pT>500 MeV and |η|<2.5
and meet the following criteria: a minimum of seven pixel
and SCT clusters, a maximum of either one pixel or two SCT
clusters shared among more than one track, and no more than
two holes6in the SCT and pixel detectors combined. The
tracks formed from the silicon detector measurements are
then extended into the TRT detector. Full details, including
a description of the TRT track extensions, can be found in
Refs. [25,26].
Figure 7shows the number of pixel clusters associated
with a muon track as a function of μ, and the unbiased resid-
ual in the local xcoordinate, which corresponds to the direc-
tion with the highest measurement precision. The unbiased
residual is the distance of the cluster from the track trajec-
tory (not including the cluster itself) at the point where that
trajectory crosses the pixel sensor. Figure 8shows the corre-
sponding quantities for the SCT. In all cases, the presampled
pile-up and standard digitisation are shown, and good agree-
ment is observed between the two methods.
Figure 9shows a comparison of the number of high-
threshold TRT drift circles as a function of μfor muons7and
electrons. As is explained above, due to the high occupancy
of the detector, the number of high-threshold drift circles is
particularly sensitive to the presampled pile-up procedure.
After the parameterised corrections discussed in Sect. 5.2
are applied, the average numbers of high-threshold drift cir-
cles for electrons and muons are each comparable for the two
methods.
The resolution of all track parameters was examined for
both methods, and they were found to agree well. Figure 10
shows the difference between the reconstructed and true val-
ues for the impact parameter of the track relative to the pri-
mary vertex (d0), measured in the transverse plane, and the
track curvature (q/ptrack
T) for muons in t¯
tevents. Finally, the
track reconstruction efficiency is shown in Fig. 11 as a func-
tion of the pTand ηof all tracks identified in t¯
tevents. The
level of agreement between the two methods is better than
0.5%.
6A hole is defined as the absence of a hit on a traversed sensitive
detector element.
7The proportion of transition radiation, and hence high-threshold hits
for pions, which dominate in pile-up events, will behave similarly to
muons due to their comparable mass. Muons are chosen here, because
their momentum spectrum in t¯
tevents is comparable to that of the
electrons and hence allows a direct comparison.
123
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Comput Softw Big Sci (2022) 6:3 Page 11 of 35 3
4
4.05
4.1
4.15
4.2
4.25
4.3
4.35
4.4
4.45
4.5
N pixel clusters
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
muon tracks
10 20 30 40 50 60 70
μ
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(a)
0
2
4
6
8
10
12
14
16
18
20
22
3
10×
mμNumber of clusters / 2
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
pixel barrel
muon tracks
50403020100 1020304050
m]μLocal x residual [
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(b)
Fig. 7 Comparison of the standard digitisation (open black circles)
and the presampled pile-up (red filled circles), showing athe average
number of pixel clusters on a track as a function of μand bthe the
local xresiduals, for tracks produced by muons in simulated t¯
tevents.
The distributions are integrated over all clusters associated with muon
tracks in the hard-scatter event. The residual is defined as the measured
hit position minus the expected hit position from the track extrapola-
tion (not including the cluster in question). The bottom panels show the
ratios of the two distributions
8
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
N SCT clusters
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
muon tracks
10 20 30 40 50 60 70
μ
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(a)
0
5
10
15
20
25
30
35
3
10×
mμNumber of clusters / 4
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
SCT barrel
muon tracks
100806040200 2040 6080100
m]μLocal x residual [
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(b)
Fig. 8 Comparison of the standard digitisation (open black circles)
and the presampled pile-up (red filled circles), showing athe average
number of SCT clusters on a track as a function of μand bthe the
local xresiduals, for tracks produced by muons in simulated t¯
tevents.
The distributions are integrated over all clusters associated with muon
tracks in the hard-scatter event. The residual is defined as the measured
hit position minus the expected hit position from the track extrapola-
tion (not including the cluster in question). The bottom panels show the
ratios of the two distributions
123
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3 Page 12 of 35 Comput Softw Big Sci (2022) 6:3
1.5
2
2.5
3
3.5
4
4.5
5
N TRT HT drift circles
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
muon tracks
10 20 30 40 50 60 70
μ
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(a)
3.5
4
4.5
5
5.5
6
6.5
7
N TRT HT drift circles
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
electron tracks
10 20 30 40 50 60 70
μ
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(b)
Fig. 9 Distributions of the average number of TRT high-threshold drift
circles, after the corrections described in the text, for tracks produced
by amuons and belectrons in simulated t¯
tevents as a function of μ.
The standard digitisation (open black circles) is compared with the pre-
sampled pile-up (red filled circles). The bottom panels show the ratios
of the two distributions
0
1
2
3
4
5
6
7
3
10×
Num. of tracks / 0.002 mm
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
muon tracks
0.040.030.020.010 0.01 0.02 0.03 0.04
[mm]
truth
0
- d
track
0
d
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(a)
0
1
2
3
4
5
6
3
10×
Num. of tracks / 0.004
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
muon tracks
0.10.050 0.05 0.1
)
truth
T
) / (q/p
truth
T
- q/p
track
T
(q/p
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(b)
Fig. 10 A comparison of the reconstructed muon track parameter resolution for ad0and bq/pTbetween the standard digitisation (open black
circles) and the presampled pile-up (red filled circles) methods, for simulated t¯
tevents. The bottom panels show the ratios of the two distributions
6 Calorimeters
6.1 Detector readout
The standard and presampled pile-up digitisation algorithms
are based on an accurate emulation of the readout of the
calorimeter system.
For the LAr calorimeter [27], the deposit of energy in the
liquid–argon gaps induces an electric current proportional to
the deposited energy. For a uniform energy deposit in the gap,
the signal has a triangular shape as a function of time with
a length corresponding to the maximum drift time of the
ionisation electrons, typically 450 ns in the EM calorime-
ter. This signal is amplified and shaped by a bipolar CR
(RC)2filter in the front-end readout boards [28] to reduce the
123
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Comput Softw Big Sci (2022) 6:3 Page 13 of 35 3
0.86
0.88
0.9
0.92
0.94
0.96
Track reco. efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
0 5 10 15 20 25 30 35 40 45 50
T
Track p
0.995
1.000
1.005
PSPU / SPU
(a)
0.78
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
Track reco. efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
2.521.510.500.5 11.522.5
ηTrack
0.995
1.000
1.005
PSPU / SPU
(b)
Fig. 11 A comparison of the track reconstruction efficiency between
the standard digitisation (open black circles) and the presampled pile-
up (red filled circles) methods, for simulated t¯
tevents as a function of
atransverse momentum and bpseudorapidity (η). All primary charged
particles with pT>500 MeV and |η|<2.5 are selected. The bottom
panels show the ratios of the two distributions
effect of out-of-time pile-up energy deposits from collisions
in the next or previous bunch crossings. To accommodate the
required dynamic range, three different gains (high, medium,
and low) are used. The shaped and amplified signals are sam-
pled at the LHC bunch-crossing frequency of 40 MHz and,
for each L1 trigger, are digitised by a 12-bit analog-to-digital
converter (ADC). The medium gain for the time sample cor-
responding to the maximum expected amplitude is digitised
first to choose the most suitable gain for a given signal. Four
time samples for the selected gain are then digitised and sent
to the back-end electronics via optical fibres. For the EMB,
EMEC, and FCAL calorimeters, the position of the maxi-
mum of the signal is in the third time sample for an energy
deposit produced in the same bunch crossing as the triggered
event. For the HEC, it is in the second time sample.
For the Tile calorimeter [29], each cell is read out by two
photomultiplier channels. The maximum height of the ana-
logue pulse in a channel is proportional to the amount of
energy deposited by the incident particle in the correspond-
ing cell. The shaped signals are sampled and digitised by
10-bit ADCs at a frequency of 40 MHz. The sampled data
are temporarily stored in a pipeline memory until an L1 trig-
ger signal is received. Seven time samples, centred around
the pulse peak, are obtained. A gain selector is used to deter-
mine which gain information is sent to the back-end elec-
tronics for event processing. By default the high-gain signal
is used, unless any of the seven time samples saturates the
ADC, at which point the low-gain signal is transmitted.
6.2 Overlay procedure
The procedure for the LAr calorimeter is described in detail
below; a very similar procedure is used for the Tile calorime-
ter.
In the presampled RDO sample, the pulse shape (ADC
data vs time sample) is stored over the time period for which
the calorimeter is read out for each calorimeter cell with-
out any zero suppression. Its computation is based on the
standard pile-up simulation, described in more detail in Ref.
[30]. It considers the energy deposited in each cell for each
bunch crossing over the time window affecting the triggered
BC, taking into account the time of each event relative to the
trigger time. The resulting pulse shape, expressed in energy
versus time, is then converted to ADC counts, applying the
energy-to-ADC calibration factor per cell and adding the
ADC pedestal. The gain used in the readout electronics for
this conversion is selected by emulating the logic applied
in the front-end readout electronics. The electronics noise is
then added to the presampled RDO, with the proper correla-
tion of the noise between the different samples, with a value
that depends on the gain used to digitise the pulse.
In the presampled pile-up step, the pulse shape of the pre-
sampled event is converted back into energy and then the
energy from the hard-scatter event is added. This is done
for each time sample, resulting in a combined pulse shape
of the hard-scatter and presampled pile-up events. From this
summed pulse shape, the energies in each time sample are
then converted back to ADC counts to produce a pulse shape
mimicking the output of the front-end electronics. The read-
123
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3 Page 14 of 35 Comput Softw Big Sci (2022) 6:3
out electronics gain used in this conversion is selected accord-
ing to the energies of the summed pulse shape. If this gain
differs from the ones used in the hard-scatter or presampled
samples, the electronics noise is corrected accordingly.
This pulse shape is then processed following exactly the
same algorithm as used in the standard pile-up digitisation,
applying the optimal filtering coefficients [31]toestimatethe
energy per cell [30]. For cells with high enough energy, the
time and pulse quality factors are also computed.
Since all cells are stored in the presampled RDO sample
without any suppression, and the energy response is perfectly
linear in the digitisation, the presampled pile-up does not rely
on any approximations except for the integer rounding that is
applied when storing ADC counts in the presampled sample.
In practice, the impact of ADC integer rounding was found
to be almost negligible. This rounding effect only applies to
the LAr case; Tile ADC data are actually stored as floats in
the presampled RDO sample.
6.3 Validation results
Figure 12a shows a comparison of the total energy deposited
in the EMB calorimeter by dijet events for the presampled
pile-up and standard digitisation methods. This distribution
is sensitive to electronics and pile-up noise and shows that
the simulation of the noise in the two methods is similar.
Figure 12b shows the distribution of a calorimeter isolation
quantity Econe20
T/ETfor simulated single-electron events.
This variable is calculated from topological clusters [32]of
energy deposits by summing the transverse energies of such
clusters within a cone of size R=0.2 around (but not
including) the candidate electron cluster. It is sensitive to pile-
up energy deposits close to the signal electrons and is again
similar for the two methods. Figure 12c shows the invariant
mass distribution of electron–positron pairs from simulated
Ze+eevents. This comparison shows that the energy
scale and resolution of electrons from signal events agree for
the two methods.
Figure 13 shows the jet response in t¯
tMC events. The
jet pTis calibrated using a multi-stage procedure [33] that
accounts for several effects, including pile-up. The pile-up
correction is performed at an early stage of the calibration
procedure and removes excess energy due to both in-time and
out-of-time pile-up. It is therefore sensitive to the details of
the pile-up emulation. The shape of the distribution (which is
sensitive to noise modelling) and the average response versus
ηover the full calorimeter acceptance are in good agreement
for the two methods. Also shown in Fig. 13 is the distribution
of missing transverse momentum Emiss
Tfor events in the same
t¯
tsample. The soft term component, as reconstructed in the
calorimeter, which is particularly sensitive to pile-up [34]is
shown as well. Again, good agreement is observed for the
two methods.
7 Muon spectrometer
The MS consists of four subdetectors: two providing high-
precision tracking measurements and two primarily provid-
ing trigger information. The technologies used in these are
different and, as with the ID, they require specific digitisation
treatments for the presampled pile-up. The main difference in
the case of the MS compared to the ID is that the occupancy
is much lower. This means that, while there is the potential
for loss of information in the presampled pile-up method if
two sub-threshold hits occur in the same detector channel, the
probability of this occurring is much lower and the resulting
effect is found to be negligible.
7.1 Detector readout and overlay procedure
7.1.1 Monitored drift tubes (MDT)
The MDTs consist of layers of drift tubes which are designed
to have a position resolution below 80 µm per tube. If a par-
ticle traverses a drift tube, ionisation is created and electrons
drift to the anode wire. If the charge at that wire exceeds a set
threshold, the charge and the time are recorded, and both are
converted to digital information. For the presampled pile-up,
the digital signals from the hard-scatter and pile-up events
are combined as follows. If a signal in a given tube is only
present in either the hard-scatter event or the pile-up event,
that signal is copied to the output RDO. If a signal is present
in both, then the two signal amplitudes are added, and the
timing is taken to be the earlier of the two events.
7.1.2 Cathode strip chambers (CSC)
The CSCs are multiwire proportional chambers with cathode
strip readout which, by charge interpolation, provide a spatial
resolution of 60 µm in the radial, or bending, plane and 5
mm in the transverse, or φ, plane. By combining the hits of a
track crossing all four chambers, a time resolution of 4 ns is
achieved, sufficient to identify the bunch crossing. For each
wire, the charge information per strip is recorded, and then
digitised and stored in four time slices, each of 50 ns. For the
presampled pile-up, the charge deposited in each strip in the
four time slices is read out for the hard-scatter event and the
pile-up event; the two signals are then added separately per
time slice and strip, taking care to ensure that the pedestal is
subtracted appropriately. The combined RDO resulting from
these summed signals is then written out.
7.1.3 Resistive plate chambers (RPC)
The RPC system covers the region |η|<1.05 and is com-
posed of gaseous parallel-plate detectors. The position reso-
lution is about 1 cm in both the transverse and longitudinal
123
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Comput Softw Big Sci (2022) 6:3 Page 15 of 35 3
0
1
2
3
4
5
6
7
3
10×
Entries / 40 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
dijet events
5000 500 1000 1500
Total energy in LAr EMB [GeV]
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(a)
2
10
3
10
4
10
5
10
Electrons / 0.05
standard (SPU)
presampled (PSPU)
ATLAS Simulation
single electrons
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
T
/E
cone20
T
Electron E
0.6
0.8
1.0
1.2
1.4
PSPU / SPU
(b)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
3
10×
Entries / GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
ee, opposite-sign pairsZ
75 80 85 90 95 100 105
Mass [GeV]
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(c)
Fig. 12 A comparison between the standard digitisation (open black
circles) and the presampled pile-up (red filled circles) for athe total
deposited energy distribution in the electromagnetic barrel of the liquid-
argon calorimeter in simulated dijet events, bthe electron isolation
Econe20
T/ETdistribution for single electrons, and cthe opposite-sign
electron-pair invariant mass distribution from simulated Ze+e
events. The normalisation of the figures is arbitrary as it is simply pro-
portional to the number of events in the MC sample. The bottom panels
show the ratios of the two distributions
directions, and the time resolution is 1.5 ns. If a muon crosses
the 2 mm space between the two parallel resistive plates, an
avalanche forms along the ionising track towards the anode.
The signal is then read out via metallic strips mounted on
the outer faces of the resistive plates if it exceeds a given
threshold; the time of the signal is also recorded. For the
presampled pile-up, the only relevant information is the time
and the overlay is performed by taking, for each channel, the
earliest signal time between the hard-scatter and the pile-up
events.
7.1.4 Thin-gap chambers (TGC)
The TGCs cover the region 1.05 <|η|<2.4. They have a
typical position resolution of 3–7 mm in the bending direction
and 2–6 mm in the transverse direction, and a time resolution
123
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3 Page 16 of 35 Comput Softw Big Sci (2022) 6:3
0
10
20
30
40
50
3
10×
Jets / 0.04
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
0.60.40.20 0.2 0.4 0.6 0.8 1
truth
T
) / p
truth
T
- p
jet
T
(p
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
/ 0.2
truth
T
) / p
truth
T
- p
jet
T
(p
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
54321012345
jet
η
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(b)
0
1
2
3
4
5
3
10×
Entries / 5 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
0 50 100 150 200 250 300
[GeV] with track-based soft term
miss
T
Total E
0.8
1.0
1.2
PSPU / SPU
(c)
0
2
4
6
8
10
12
14
16
3
10×
Entries / 5 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
0 102030405060708090100
term [GeV]
miss
T
Cluster-based soft E
0.8
1.0
1.2
PSPU / SPU
(d)
Fig. 13 A comparison between the standard digitisation (open black
circles) and the presampled pile-up (red filled circles) in simulated t¯
t
events for athe jet pTresponse, bthe mean jet pTresponse as a func-
tion of jet pseudorapidity ηjet,cthe total Emiss
Tdistribution, and dthe
component of the Emiss
Tfrom energy clusters in the calorimeter that are
not associated with calibrated physics objects, known as the soft term.
The bottom panels show the ratios of the two distributions
of 4 ns. The radial coordinate is measured by reading which
TGC wire-group is hit; the azimuthal coordinate is measured
by reading which radial strip is hit. For each wire, the time
at which a signal is above threshold is recorded and digitised
and then written in the digit format. As in the RPCs, the hard-
scatter and pile-up events are combined by taking the earliest
arrival time of any hard-scatter or pile-up signal for a given
wire.
7.2 Validation results
The presampled pile-up procedure is validated using muons
from simulated Zμ+μevents and comparing their
characteristics with those after the standard pile-up digitisa-
tion procedure. Figure 14 shows the reconstruction efficiency
of muons as a function of pTand ηfor the two methods. They
agree to better than 0.1% for nearly the entire pTand ηrange.
Figure 14c shows the invariant mass of the two muons for the
same event sample. Also here, good agreement is observed
between the two methods.
123
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Comput Softw Big Sci (2022) 6:3 Page 17 of 35 3
0.6
0.7
0.8
0.9
1
1.1
1.2
Reconstruction efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
μμ →Z
prompt muons
5 6 7 10 20 30 100 200
[GeV]
T
p
0.996
0.998
1.000
1.002
1.004
PSPU / SPU
(a)
0.6
0.7
0.8
0.9
1
1.1
1.2
Reconstruction efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
μμ →Z
prompt muons
2.521.510.50 0.5 1 1.5 2 2.5
η
0.996
0.998
1.000
1.002
1.004
PSPU / SPU
(b)
0
2
4
6
8
10
12
14
16
18
3
10×
Entries / 1.5 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
μμ →Z
80 85 90 95 100 105
Mass [GeV]
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(c)
Fig. 14 The muon reconstruction efficiency versus apTand bηand cthe dimuon invariant mass in simulated Zμ+μevents. The open
black circles correspond to the standard digitisation and the red filled circles to presampled pile-up. The bottom panels show the ratios of the
corresponding distributions
8 Trigger
The L1 trigger receives inputs from the L1 calorimeter
(L1Calo) and L1 muon triggers. The L1Calo decision is
formed using reduced granularity inputs from the LAr and
Tile calorimeters. The L1 muon trigger receives signals from
the RPCs in the barrel and from the TGCs in the endcaps
as is described in Sect. 7. After the L1 trigger decision, the
HLT has access to the data from the full detector to perform a
refined analysis. The trigger decisions and all reconstructed
objects are stored in a dedicated record of the accepted event.
The L1 hardware trigger is simulated using dedicated
algorithms that strive to perform a bit-wise correct emula-
tion of the trigger decision including any trigger objects that
the hardware produces. The HLT runs on the output of the
L1 trigger using the same simulation software as used for
data. The following sections discuss the L1 calorimeter trig-
ger and the overall HLT performance. No dedicated changes
were required to the muon trigger simulation beyond what is
discussed for the general simulation in Sect. 7. While the HLT
software itself remains unchanged between the two methods,
it depends on the inputs from the various subdetectors that
do differ and hence serves as an additional validation.
123
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2
10
3
10
4
10
5
10
Number of RoIs / 0.5 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
eeZ
012345678910
EM isolation sum [GeV]
0.6
0.8
1.0
1.2
1.4
PSPU / SPU
(a)
2
10
3
10
4
10
5
10
Number of RoIs / 0.5 GeV
standard (SPU)
presampled (PSPU)
ATLAS Simulation
eeZ
012345678910
Hadronic isolation sum [GeV]
0.6
0.8
1.0
1.2
1.4
PSPU / SPU
(b)
Fig. 15 Distributions of ETin the isolation regions of the L1Calo
etrigger: ain the electromagnetic calorimeter and bin the hadronic
calorimeter. The standard digitisation (black open circles) is compared
with the presampled pile-up (red filled circles). The distributions are for
regions around electrons in Ze+eevents, which are dominated
by electronic noise and pile-up. The bottom panels show the ratios of
the two distributions
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Trigger efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
eeZ
HLT_e26_lhtight_nod0_ivarloose
20 40 60 80 100 120 140
[GeV]
T
Offline isolated electron E
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(a)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Trigger efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
eeZ
HLT_e26_lhtight_nod0_ivarloose
10 20 30 40 50 60 70
>μ<
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(b)
Fig. 16 The combined L1 and HLT trigger efficiency of the 28 GeV
electron trigger from simulated Ze+eevents (red filled circles)
as a function of aETand bpile-up for the standard digitisation (open
black circles) and presampled pile-up (red filled circles). The bottom
panels show the ratios of the two distributions
8.1 L1 calorimeter trigger simulation
The inputs to the L1Calo trigger processors are trigger towers
[17]. These are formed in the on-detector electronics by sum-
mation of the analogue voltage pulses from calorimeter cells
in groups of η ×φ 0.1×π/32, separately in the elec-
tromagnetic and hadronic calorimeter systems. These signals
are then transmitted over 70 m long, individually shielded,
twisted-pair cables to the trigger electronics, where they are
digitised with a least-count equivalent to 250 MeV trans-
verse energy and a sampling frequency of 40 MHz. A cus-
tom digital processor, consisting of filters, comparators, and
look-up tables, analyzes the shape of the digitised pulse to
identify which bunch crossing it came from. It also corrects
123
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Comput Softw Big Sci (2022) 6:3 Page 19 of 35 3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Trigger efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
μμ →Z
HLT_mu26_ivarmedium
20 30 40 50 60 70 80 90 100
[GeV]
T
Offline isolated muon p
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(a)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Trigger efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
μμ →Z
HLT_mu26_ivarmedium
10 20 30 40 50 60 70
>μ<
0.90
0.95
1.00
1.05
1.10
PSPU / SPU
(b)
Fig. 17 The combined L1 and HLT trigger efficiency of the 26 GeV muon trigger from simulated Zμ+μevents as a function of apTand b
pile-up for the standard digitisation (open black circles) and presampled pile-up (red filled circles). The bottom panels show the ratios of the two
distributions
1
10
1
10
2
10
3
10
4
10
Events
standard (SPU)
presampled (PSPU)
ATLAS Simulation
Pythia QCD
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
[TeV]
T
Leading trigger jet p
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(a)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Trigger efficiency
standard (SPU)
presampled (PSPU)
ATLAS Simulation
t8aihtyP+gehwoP
t
30 40 50 60 70 80 90 100
[GeV]
T
6th leading offline jet p
0.8
0.9
1.0
1.1
1.2
PSPU / SPU
(b)
Fig. 18 a The pTof the leading jet in all events with a triggered jet and
bthe trigger efficiency of the six-jet trigger requiring a jet pT>45 GeV
as a function of the pTof the sixth leading jet for the standard digiti-
sation (open black circles) and presampled pile-up (red filled circles).
The bottom panels show the ratios of the two distributions
for shifts in the baseline caused by the interplay of in-time
and out-of-time pile-up due to the LHC bunch structure, sub-
tracts pedestals, and applies noise cuts. Finally, it provides
calibrated transverse energy ETvalues for use in the trig-
ger algorithms on scales of 500 MeV/count for the electron,
photon, and τ-lepton triggers and 1 GeV/count for all other
triggers.
In the simulation, the analogue signals received from the
calorimeters are represented by objects containing a vector of
floating-point values, corresponding to the amplitudes of the
pulses sampled at 25 ns intervals. These are then quantised,
with the addition of noise from the digitisation system, and
passed through a precise simulation of the signal processing
performed by the trigger electronics. The calorimeter objects
123
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3 Page 20 of 35 Comput Softw Big Sci (2022) 6:3
are formed from calorimeter hits, using a model of the pulse
shaping and the noise from the readout and summation chain.
For presampled pile-up, the analogue calorimeter objects
are merged before the trigger digitisation and processing are
performed. This then allows the unmodified trigger simula-
tion to be performed on the merged data, and it avoids any
possible bias due to merging data that have been quantised
on a relatively coarse scale. The merging is performed by an
additional algorithm, which is run during the pile-up merg-
ing prior to the trigger simulation to create a set of merged
calorimeter towers. The merging itself uses the calorimeter
object identifiers to match corresponding towers in the hard-
scatter and pile-up event collections, and the amplitudes of
the signals of the same towers in both events are summed. A
new collection of objects containing the summed amplitudes
is then created and written to the output stream.
Figure 15 shows the L1Calo ETdistributions in isolation
regions around electrons in Ze+eevents, which are
sensitive to the pile-up ETdeposits close to the electrons.
Good agreement is seen between the standard and presam-
pled pile-up simulation chains.
8.2 HLT simulation and performance
After being accepted by the L1 trigger, the events are pro-
cessed by the HLT using finer granularity calorimeter infor-
mation, precision measurements from the muon system, and
tracking information from the inner detector. As needed, the
HLT reconstruction can be executed either for the full event
or within smaller, isolated regions of interest (RoIs) identified
by the L1 trigger. To reduce the processing time, most HLT
triggers use a two-stage approach with a fast (trigger-specific)
first-pass reconstruction to reject the majority of events and a
slower, higher precision (offline-like) reconstruction for the
remaining events.
The reconstruction of electron (muon) candidates requires
the matching of a calorimeter cluster (muon spectrometer
track) to a track in the inner detector and is therefore sensi-
tive to changes in the inner detector, calorimeter, and muon
spectrometer reconstruction. Figure 16 shows the trigger effi-
ciency of the primary 28 GeV electron trigger measured with
simulated Ze+eevents for the standard and presam-
pled pile-up simulation chains. Similarly, Fig. 17 shows the
trigger efficiency of the primary 26 GeV muon trigger mea-
sured with simulated Zμ+μevents. No significant dif-
ferences are observed in the trigger efficiency between the
presampled and standard pile-up simulation chains.
Jet and Emiss
Ttriggers are mainly based on the calorime-
ter reconstruction and are especially sensitive to changes in
the simulation of low-pTjets. Figure 18 shows the pTdis-
tribution of the leading jet and the trigger efficiency as a
function of the sixth leading jet pTfor a multi-jet trigger
requiring six jets with a pTlarger than 45 GeV. Good agree-
ment between the standard and presampled pile-up simula-
tion chains is observed in both cases.
All other triggers relevant to the ATLAS physics pro-
gramme were also studied and no notable differences
between the two methods were observed.
9 Conclusions
A new method for reproducing the impact of pile-up interac-
tions on the ATLAS detector performance is presented, based
on overlaying presampled pile-up events on the hard-scatter
event of interest during the digitisation. The method is val-
idated separately for each ATLAS detector system and the
trigger. In all cases, it is possible to achieve good agreement
with the standard pile-up simulation chain which has been
used up to now. For a large variety of quantities, detailed com-
parisons are made between the two methods, and all the dif-
ferences are found to be small, so that the impact on physics
analyses is considered negligible.
The presampled pile-up method is shown to use signif-
icantly less computing resources than the standard method
used so far within ATLAS. For the Run 2 pile-up distribution
and software, the CPU resources required for the entire MC
simulation chain are reduced by around 20%.
Acknowledgements We thank CERN for the very successful operation
of the LHC, as well as the support staff from our institutions without
whom ATLAS could not be operated efficiently.
We acknowledge the support of ANPCyT, Argentina; YerPhI, Arme-
nia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan;
SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI,
Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Min-
ciencias, Colombia; MSMT CR, MPO CR and VSC CR, Czech Repub-
lic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU,
France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT,
Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center,
Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO,
Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT,Portugal;
MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Fed-
eration; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia;
DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foun-
dation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzer-
land; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE
and NSF, United States of America. In addition, individual groups and
members have received support from BCKDF, CANARIE, Compute
Canada, CRC and IVADO, Canada; Beijing Municipal Science & Tech-
nology Commission, China; COST, ERC, ERDF, Horizon 2020 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 GIF, Israel; La Caixa Banking Foundation, CERCA Pro-
gramme Generalitat de Catalunya and PROMETEO and GenT Pro-
grammes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse,
Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
The crucial computing support from all WLCG partners is acknowl-
edged gratefully, in particular from CERN, the ATLAS Tier-1 facili-
ties at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-
123
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Comput Softw Big Sci (2022) 6:3 Page 21 of 35 3
IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-
T1 (Netherlands), PIC (Spain), ASGC (Taiwan), 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.
[35].
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References
1. Sjöstrand T, Mrenna S, Skands P (2008) A brief introduction to
PYTHIA 8.1. Comput Phys Commun 178:852. arXiv:0710.3820
[hep-ph]
2. ATLAS Collaboration (2012) Letter of intent for the phase-
II upgrade of the ATLAS experiment, CERN-LHCC-2012-022.
https://cds.cern.ch/record/1502664
3. Donnachie A, Landshoff P (1992) Total cross sections. Phys
Lett B 296:227. https://doi.org/10.1016/0370-2693(92)90832-O.
ISSN:0370-2693
4. ATLAS Collaboration (2019) Luminosity Results for
Run 2. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/
LuminosityPublicResultsRun2
5. Gleisberg T et al (2009) Event generation with SHERPA 1.1. JHEP
02:007 arXiv:0811.4622 [hep-ph]
6. GEANT4 Collaboration, Agostinelli S et al (2003) Geant4—a sim-
ulation toolkit. Nucl Instrum Meth A 506:250
7. ATLAS Collaboration (2010) The ATLAS simulation infrastruc-
ture. Eur Phys J C 70:823. arXiv:1005.4568 [physics.ins-det]
8. Lange DJ, Hildreth M, Ivantchenko VN, Osborne I (2015)
Upgrades for the CMS simulation. J Phys Conf Ser 608:012056
(ed. by L. Fiala, M. Lokajicek and N. Tumova)
9. ATLAS Collaboration (2017) Reconstruction of primary vertices
at the ATLAS experiment in Run 1 proton-proton collisions at the
LHC. Eur Phys J C 77:332. arXiv:1611.10235 [hep-ex]
10. ATLAS Collaboration (2019) Electron and photon performance
measurements with the ATLAS detector using the 2015–2017 LHC
proton–proton collision data. JINST 14:P12006. arXiv:1908.00005
[hep-ex]
11. ATLAS Collaboration (2016) Muon reconstruction performance
of the ATLAS detector in proton-proton collision data at s=13
TeV. Eur Phys J C 76:292. arXiv:1603.05598 [hep-ex]
12. ATLAS Collaboration (2020) Jet energy scale and resolution mea-
sured in proton–proton collisions at s=13 TeV with the ATLAS
detector. arXiv:2007.02645 [hep-ex]
13. ATLAS Collaboration (2019) ATLAS b-jet identification perfor-
mance and efficiency measurement with t¯
tevents in pp collisions
at s=13 TeV. Eur Phys J C 79:970. arXiv:1907.05120 [hep-ex]
14. ATLAS Collaboration (2017) Measurement of the tau lepton recon-
struction and identification performance in the ATLAS experiment
using pp collisions at s=13 TeV, ATLAS-CONF-2017-029.
https://cds.cern.ch/record/2261772
15. Calafiura P, Catmore J, Costanzo D, Di Girolamo A (2020) ATLAS
HL-LHC Computing Conceptual Design Report. https://cds.cern.
ch/record/2729668
16. ATLAS Collaboration (2008) The ATLAS experiment at the CERN
large hadron collider. JINST 3:S08003
17. ATLAS Collaboration (2017) Performance of the ATLAS trigger
system in 2015. Eur Phys J C 77:317. arXiv:1611.09661 [hep-ex]
18. ATLAS Collaboration (2010) ATLAS insertable B-layer: technical
design report, ATLAS-TDR-19; CERN-LHCC-2010-013 (2010).
https://cds.cern.ch/record/1291633 [Addendum: ATLAS-TDR-
19-ADD-1; CERN-LHCC-2012-009 (2012). https://cds.cern.ch/
record/1451888]
19. Abbott B et al (2018) Production and integration of the
ATLAS Insertable B-Layer. JINST 13:T05008. arXiv:1803.00844
[physics.ins-det]
20. ATLAS Collaboration (2005) ATLAS Computing: Techni-
cal Design Report, ATLAS-TDR-17; CERN-LHCC-2005-022.
https://cds.cern.ch/record/837738
21. Ball RD et al (2013) Parton distributions with LHC data. Nucl Phys
B 867:244 arXiv:1207.1303 [hep-ph]
22. ATLAS Collaboration (2016) The Pythia 8 A3 tune description
of ATLAS minimum bias and inelastic measurements incorpo-
rating the Donnachie–Landshoff diffractive model, ATL-PHYS-
PUB-2016-017. https://cds.cern.ch/record/2206965
23. ATLAS Collaboration (2016) Luminosity determination in pp col-
lisions at p s=8 TeV using the ATLAS detector at the LHC.
Eur Phys J C 76:653. arXiv:1608.03953 [hep-ex]
24. ATLAS Collaboration, Hit occupancy in Pixel and IBL in 2016 and
2018 (2018). https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/
PLOTS/PIX-2016-007/
25. Cornelissen T et al (2008) The new ATLAS track reconstruction
(NEWT). J Phys Conf Ser 119:032014 (ed. by R. Sobie, R. Tafirout
and J. Thomson)
26. ATLAS Collaboration (2017) Performance of the ATLAS track
reconstruction algorithms in dense environments in LHC Run 2.
Eur Phys J C 77:673. arXiv:1704.07983 [hep-ex]
27. ATLAS Collaboration (2010) Readiness of the ATLAS liquid
argon calorimeter for LHC collisions. Eur Phys J C 70:723.
arXiv:0912.2642 [hep-ex]
28. Abreu H et al (2010) Performance of the electronic readout of the
ATLAS liquid argon calorimeters. JINST 5:P09003
29. ATLAS Collaboration (2018) Operation and performance of the
ATLAS Tile Calorimeter in Run 1. Eur Phys J C 78:987.
arXiv:1806.02129 [hep-ex]
30. Lampl W et al (2007) Digitization of LAr calorimeter for CSC sim-
ulations. ATL-LARG-PUB-2007-011. https://cds.cern.ch/record/
1057879
31. Cleland WE, Stern EG (1994) Signal processing considerations
for liquid ionization calorimeters in a high rate environment. Nucl
Instrum Meth A 338:467
32. ATLAS Collaboration (2017) Topological cell clustering in the
ATLAS calorimeters and its performance in LHC Run 1. Eur Phys
J C 77:490. arXiv:1603.02934 [hep-ex]
33. ATLAS Collaboration (2017) Jet energy scale measurements
and their systematic uncertainties in proton–proton collisions at
s=13 TeV with the ATLAS detector. Phys Rev D 96:072002.
arXiv:1703.09665 [hep-ex]
34. ATLAS Collaboration (2018) Performance of missing transverse
momentum reconstruction with the ATLAS detector using proton-
proton collisions at s=13 TeV. Eur Phys J C 78:903.
arXiv:1802.08168 [hep-ex]
35. ATLAS Collaboration, ATLAS Computing Acknowledgements,
ATL-SOFT-PUB-2020-001. https://cds.cern.ch/record/2717821
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3 Page 22 of 35 Comput Softw Big Sci (2022) 6:3
ATLAS Collaboration
G. Aad101 , B. Abbott127 , D. C. Abbott102 , A. Abed Abud36 , K. Abeling53 , D. K. Abhayasinghe93 ,
S. H. Abidi29 , O. S. AbouZeid40 , N. L. Abraham155, H. Abramowicz160 , H. Abreu159 , Y. Abulaiti6,
A. C. Abusleme Hoffman145a , B. S. Acharya66a,66b,o, B. Achkar53 , L. Adam99 , C. Adam Bourdarios5,
L. Adamczyk83a , L. Adamek165 , J. Adelman120 , A. Adiguzel12c,ad , S. Adorni54 , T. Adye142 ,
A. A. Affolder144 ,Y.Ak
159 , C. Agapopoulou64 , M.N.Agaras
38 , A. Aggarwal118 , C. Agheorghiesei27c ,
J. A. Aguilar-Saavedra138f,138a,ac , A. Ahmad36 , F. Ahmadov79 , W. S. Ahmed103 ,X.Ai
46 , G. Aielli73a ,73b ,
S. Akatsuka85 , M. Akbiyik99 , T.P.Akesson
96 , E. Akilli54 , A.V.Akimov
110 , K. Al Khoury39 ,
G. L. Alberghi23b,23a , J. Albert174 , M. J. Alconada Verzini160 , S. Alderweireldt36 , M. Aleksa36 ,
I. N. Aleksandrov79 ,C.Alexa
27b , T. Alexopoulos10 , A. Alfonsi119 , F. Alfonsi23b ,23a , M. Alhroob127 ,
B. Ali140 ,S.Ali
157 , M. Aliev164 , G. Alimonti68a , C. Allaire36 , B.M.M.Allbrooke
155 , P. P. Allport21 ,
A. Aloisio69a,69b , F. Alonso88 , C. Alpigiani147 , E. Alunno Camelia73a,73b, M. Alvarez Estevez98 ,
M. G. Alviggi69a,69b , Y. Amaral Coutinho80b , A. Ambler103 , L. Ambroz133 , C. Amelung36, D. Amidei105 ,
S. P. Amor Dos Santos138a , S. Amoroso46 , C. S. Amrouche54, C. Anastopoulos148 , N. Andari143 , T. Andeen11 ,
J. K. Anders20 , S. Y. Andrean45a,45b , A. Andreazza68a,68b , V. Andrei61a, C. R. Anelli174, S. Angelidakis9,
A. Angerami39 , A. V. Anisenkov121b,121a , A. Annovi71a , C. Antel54 , M. T. Anthony148 , E. Antipov128 ,
M. Antonelli51 , D.J.A.Antrim
18 , F. Anulli72a , M. Aoki81 , J. A. Aparisi Pozo172 , M.A.Aparo
155 ,
L. Aperio Bella46 , N. Aranzabal36 , V. Araujo Ferraz80a , C. Arcangeletti51 , A.T.H.Arce
49 ,J-
F. Arguin109 , S. Argyropoulos52 , J.-H. Arling46 , A.J.Armbruster
36 , A. Armstrong169 , O. Arnaez165 ,
H. Arnold36 , Z. P. Arrubarrena Tame113 , G. Artoni133 , H. Asada116 ,K.Asai
125 ,S.Asai
162 ,
N. A. Asbah59 , E. M. Asimakopoulou170 , L. Asquith155 , J. Assahsah35e , K. Assamagan29 , R. Astalos28a ,
R. J. Atkin33a,33b,33c,33d ,33e,33f , M. Atkinson171, N. B. Atlay19 , H. Atmani64, P. A. Atmasiddha105, K. Augsten140 ,
V. A. Austrup180 , G. Avolio36 , M. K. Ayoub15c , G. Azuelos109,ak , D. Babal28a , H. Bachacou143 ,
K. Bachas161 , F. Backman45a,45b , P. Bagnaia72a,72b , H. Bahrasemani151, A.J.Bailey
172 , V. R. Bailey171 ,
J. T. Baines142 , C. Bakalis10 , O.K.Baker
181 , P. J. Bakker119 , E. Bakos16 , D. Bakshi Gupta8, S. Balaji156 ,
R. Balasubramanian119 , E. M. Baldin121b ,121a , P. Balek178 , F. Balli143 , W. K. Balunas133 ,J.Balz
99 ,
E. Banas84 , M. Bandieramonte137 , A. Bandyopadhyay19 , L. Barak160 , W. M. Barbe38 , E. L. Barberio104 ,
D. Barberis55b,55a , M. Barbero101 , G. Barbour94, K. N. Barends33a , T. Barillari114 , M-S. Barisits36 ,
J. Barkeloo130 , T. Barklow152 , B. M. Barnett142 , R. M. Barnett18 , Z. Barnovska-Blenessy60a , A. Baroncelli60a ,
G. Barone29 , A.J.Barr
133 , L. Barranco Navarro45a,45b ,F.Barreiro
98 , J. Barreiro Guimarães da Costa15a ,
U. Barron160 ,S.Barsov
136 , F. Bartels61a , R. Bartoldus152 , G. Bartolini101 ,A.E.Barton
89 ,P.Bartos
28a ,
A. Basalaev46 , A. Basan99 , A. Bassalat64 ,ah ,M.J.Basso
165 , C. R. Basson100 , R. L. Bates57 , S. Batlamous35f ,
J. R. Batley32 , B. Batool150 , M. Battaglia144, M. Bauce72a,72b , F. Bauer143,, P. Bauer24 ,H.S.Bawa
31,
A. Bayirli12c , J. B. Beacham49 , T. Beau134 , P. H. Beauchemin168 , F. Becherer52 , P. Bechtle24 , H. P. Beck20,q,
K. Becker176 , C. Becot46 , A. J. Beddall12a , V. A. Bednyakov79 ,C.P.Bee
154 , T. A. Beermann180 ,
M. Begalli80b , M. Begel29 , A. Behera154 , J. K. Behr46 ,J.F.Beirer
53,36 , F. Beisiegel24 ,M.Belfkir
5,
G. Bella160 , L. Bellagamba23b , A. Bellerive34 , P. Bellos21 , K. Beloborodov121b,121a , K. Belotskiy111 ,
N. L. Belyaev111 , D. Benchekroun35a,35b,35c,35d,35e,35f , N. Benekos10 , Y. Benhammou160 , D. P. Benjamin6,
M. Benoit29 , J. R. Bensinger26 , S. Bentvelsen119 , L. Beresford133 , M. Beretta51 , D. Berge19 ,
E. Bergeaas Kuutmann170 , N. Berger5, B. Bergmann140 , L.J.Bergsten
26 , J. Beringer18 , S. Berlendis7,
G. Bernardi134 , C. Bernius152 , F. U. Bernlochner24 ,T.Berry
93 ,P.Berta
46 , A. Berthold48 , I. A. Bertram89 ,
O. Bessidskaia Bylund180 , S. Bethke114 , A. Betti42 , A.J.Bevan
92 , S. Bhatta154 , D. S. Bhattacharya175 ,
P. Bhattarai26, V. S. Bhopatkar6,R.Bi
137, R. M. Bianchi137 , O. Biebel113 , R. Bielski36 , K. Bierwagen99 ,
N. V. Biesuz71a,71b , M. Biglietti74a , T.R.V.Billoud
140 , M. Bindi53 , A. Bingul12d ,C.Bini
72a,72b ,
S. Biondi23b,23a , C.J.Birch-sykes
100 ,M.Birman
178 , T. Bisanz36, J.P.Biswal
3,D.Biswas
179,j,
A. Bitadze100 , C. Bittrich48 ,K.Bjørke
132 , T. Blazek28a , I. Bloch46 , C. Blocker26 ,A.Blue
57 ,
U. Blumenschein92 , G. J. Bobbink119 , V. S. Bobrovnikov121b,121a , D. Bogavac14 , A. G. Bogdanchikov121b,121a ,
C. Bohm45a, V. Boisvert93 , P. Bokan170,53 ,T.Bold
83a , M. Bomben134 , M. Bona92 , J. S. Bonilla130 ,
M. Boonekamp143 , C. D. Booth93 , A. G. Borbély57 , H. M. Borecka-Bielska90 , L. S. Borgna94 , G. Borissov89 ,
D. Bortoletto133 , D. Boscherini23b ,M.Bosman
14 , J. D. Bossio Sola103 , K. Bouaouda35a , J. Boudreau137 ,
E. V. Bouhova-Thacker89 , D. Boumediene38 , R. Bouquet134 , A. Boveia126 ,J.Boyd
36 ,D.Boye
29 ,
I. R. Boyko79 , A. J. Bozson93 , J. Bracinik21 , N. Brahimi60d ,60c , G. Brandt180 , O. Brandt32 ,F.Braren
46 ,
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Comput Softw Big Sci (2022) 6:3 Page 23 of 35 3
B. Brau102 ,J.E.Brau
130 , W. D. Breaden Madden57, K. Brendlinger46 , R. Brener159 , L. Brenner36 ,
R. Brenner170 , S. Bressler178 , B. Brickwedde99 , D.L.Briglin
21 , D. Britton57 , D. Britzger114 , I. Brock24 ,
R. Brock106 , G. Brooijmans39 , W. K. Brooks145d ,E.Brost
29 , P. A. Bruckman de Renstrom84 , B. Brüers46 ,
D. Bruncko28b , A. Bruni23b , G. Bruni23b , M. Bruschi23b , N. Bruscino72a,72b , L. Bryngemark152 ,
T. Buanes17 , Q. Buat154 , P. Buchholz150 , A. G. Buckley57 , I. A. Budagov79 , M. K. Bugge132 ,
O. Bulekov111 , B.A.Bullard
59 , T.J.Burch
120 , S. Burdin90 , C.D.Burgard
46 , A. M. Burger128 ,
B. Burghgrave8, J.T.P.Burr
46 ,C.D.Burton
11 , J. C. Burzynski102 , V. Büscher99 , E. Buschmann53,
P. J. Bussey57 , J.M.Butler
25 , C.M.Buttar
57 , J. M. Butterworth94 , W. Buttinger142 , C. J. Buxo Vazquez106 ,
A. R. Buzykaev121b,121a , G. Cabras23b ,23a , S. Cabrera Urbán172 , D. Caforio56 ,H.Cai
137 ,V.M.M.Cairo
152 ,
O. Cakir4a , N. Calace36 , P. Calafiura18 , G. Calderini134 , P. Calfayan65 , G. Callea57 , L. P. Caloba80b,
A. Caltabiano73a,73b, S. Calvente Lopez98 ,D.Calvet
38 ,S.Calvet
38 , T.P.Calvet
101 , M. Calvetti71a,71b ,
R. Camacho Toro134 , S. Camarda36 , D. Camarero Munoz98 , P. Camarri73a,73b , M. T. Camerlingo74a,74b ,
D. Cameron132 , C. Camincher36 , M. Campanelli94 , A. Camplani40 , V. Canale69a,69b , A. Canesse103 ,
M. Cano Bret77 , J. Cantero128 ,Y.Cao
171 , M. Capua41b ,41a , R. Cardarelli73a , F. Cardillo172 ,
G. Carducci41b,41a ,T.Carli
36 , G. Carlino69a , B. T. Carlson137 , E.M.Carlson
174,166a , L. Carminati68a,68b ,
M. Carnesale72a,72b , R.M.D.Carney
152 , S. Caron118 , E. Carquin145d ,S.Carrá
46 , G. Carratta23b,23a ,
J. W. S. Carter165 , T.M.Carter
50 , M. P. Casado14,g, A. F. Casha165, E. G. Castiglia181 , F. L. Castillo172 ,
L. Castillo Garcia14 , V. Castillo Gimenez172 ,N. F. Castro
138a,138e , A. Catinaccio36 ,J. R.Catmore
132 , A. Cattai36,
V. Cavaliere29 , V. Cavasinni71a,71b , E. Celebi12b , F. Celli133 ,K.Cerny
129 , A. S. Cerqueira80a ,A.Cerri
155 ,
L. Cerrito73a,73b , F. Cerutti18 , A. Cervelli23b,23a , S. A. Cetin12b , Z. Chadi35a, D. Chakraborty120 , M. Chala138f ,
J. Chan179 , W. S. Chan119 , W.Y.Chan
90 , J. D. Chapman32 , B. Chargeishvili158b , D. G. Charlton21 ,
T. P. Charman92 , M. Chatterjee20 , C. C. Chau34 , S. Chekanov6, S. V. Chekulaev166a , G. A. Chelkov79,af ,
B. Chen78 , C. Chen60a, C. H. Chen78 , H. Chen15c , H. Chen29 , J. Chen60a , J. Chen39 , J. Chen26 ,
S. Chen135 , S. J. Chen15c , X. Chen15b , Y. Chen60a , Y-H. Chen46 , C. L. Cheng179 , H. C. Cheng62a ,
H. J. Cheng15a , A. Cheplakov79 , E. Cheremushkina122 , R. Cherkaoui El Moursli35f , E. Cheu7, K. Cheung63 ,
L. Chevalier143 , V. Chiarella51 , G. Chiarelli71a , G. Chiodini67a , A. S. Chisholm21 , A. Chitan27b ,
I. Chiu162 , Y.H.Chiu
174 , M. V. Chizhov79,s, K. Choi11 , A. R. Chomont72a,72b , Y. Chou102 ,Y.S.Chow
119,
L. D. Christopher33f , M.C.Chu
62a ,X.Chu
15a,15d , J. Chudoba139 , J. J. Chwastowski84 ,D.Cieri
114 ,
K. M. Ciesla84 , V. Cindro91 ,I.A.Cioar˘a27b , A. Ciocio18 , F. Cirotto69a,69b ,Z.H.Citron
178,k, M. Citterio68a ,
D. A. Ciubotaru27b, B. M. Ciungu165 ,A.Clark
54 ,P.J.Clark
50 ,S.E.Clawson
100 , C. Clement45a,45b ,
L. Clissa23b,23a , Y. Coadou101 , M. Cobal66a,66c , A. Coccaro55b , J. Cochran78, R. Coelho Lopes De Sa102 ,
S. Coelli68a , H. Cohen160, A.E.C.Coimbra
36 ,B.Cole
39 , J. Collot58 , P. Conde Muiño138a,138h ,
S. H. Connell33c , I. A. Connelly57 , F. Conventi69a ,al , A. M. Cooper-Sarkar133 , F. Cormier173 , L. D. Corpe94 ,
M. Corradi72a,72b ,E.E.Corrigan
96 , F. Corriveau103,aa , M.J.Costa
172 , F. Costanza5, D. Costanzo148 ,
G. Cowan93 , J.W.Cowley
32 , J. Crane100 , K. Cranmer124 , R. A. Creager135 , S. Crépé-Renaudin58 ,
F. Crescioli134 , M. Cristinziani150 , M. Cristoforetti75a ,75b , V. Croft168 , G. Crosetti41b,41a , A. Cueto5,
T. Cuhadar Donszelmann169 ,H.Cui
15a,15d, A. R. Cukierman152 , W. R. Cunningham57 , S. Czekierda84 ,
P. Czodrowski36 , M. M. Czurylo61b , M. J. Da Cunha Sargedas De Sousa60b , J. V. Da Fonseca Pinto80b ,
C. Da Via100 , W. Dabrowski83a , T. Dado47 , S. Dahbi33f ,T.Dai
105 , C. Dallapiccola102 ,M.Dam
40 ,
G. D’amen29 , V. D’Amico74a,74b ,J.Damp
99 , J. R. Dandoy135 , M. F. Daneri30 , M. Danninger151 ,
V. Da o36 , G. Darbo55b , A. Dattagupta130 ,S.DAuria
68a,68b ,C.David
166b , T. Davidek141 ,D.R.Davis
49 ,
I. Dawson148 ,K.De
8, R. De Asmundis69a , M. De Beurs119 ,S.DeCastro
23b,23a , N. De Groot118 ,
P. de Jong119 ,H.DelaTorre
106 ,A.DeMaria
15c , D. De Pedis72a , A. De Salvo72a , U. De Sanctis73a ,73b ,
M. De Santis73a,73b , A. De Santo155 ,J.B.DeVivieDeRegie
58 , D. V. Dedovich79, J. Degens119 ,
A. M. Deiana42 , J. Del Peso98 , Y. Delabat Diaz46 , F. Deliot143 , C. M. Delitzsch7, M. Della Pietra69a ,69b ,
D. Della Volpe54 , A. Dell’Acqua36 , L. Dell’Asta68a,68b ,M.Delmastro
5, C. Delporte64, P. A. Delsart58 ,
S. Demers181 , M. Demichev79 , G. Demontigny109, S. P. Denisov122 ,L.DEramo
120 , D. Derendarz84 ,
J. E. Derkaoui35e , F. Derue134 ,P.Dervan
90 , K. Desch24 , K. Dette165 , C. Deutsch24 ,P.O.Deviveiros
36 ,
F. A. Di Bello72a ,72b , A. Di Ciaccio73a ,73b , L. Di Ciaccio5, C. Di Donato69a ,69b , A.DiGirolamo
36 ,
G. Di Gregorio71a,71b , A. Di Luca75a,75b , B. Di Micco74a,74b ,R.DiNardo
74a,74b , C. Diaconu101 ,F.A.Dias
119 ,
T. Dias Do Vale138a ,M.A.Diaz
145a , F. G. Diaz Capriles24 , J. Dickinson18 , M. Didenko164 , E. B. Diehl105 ,
J. Dietrich19 , S. Díez Cornell46 , C. Diez Pardos150 , A. Dimitrievska18 ,W.Ding
15b , J. Dingfelder24 ,
S. J. Dittmeier61b , F. Dittus36 ,F.Djama
101 , T. Djobava158b , J. I. Djuvsland17 , M.A.B.DoVale
146 ,
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3 Page 24 of 35 Comput Softw Big Sci (2022) 6:3
M. Dobre27b , D. Dodsworth26 , C. Doglioni96 , J. Dolejsi141 , Z. Dolezal141 , M. Donadelli80c , B. Dong60c ,
J. Donini38 , A. D’onofrio15c , M. D’Onofrio90 , J. Dopke142 ,A.Doria
69a , M.T.Dova
88 ,A.T.Doyle
57 ,
E. Drechsler151 , E. Dreyer151 , T. Dreyer53 , A. S. Drobac168 ,D.Du
60b ,T.A.duPree
119 , Y. Duan60d ,
F. Dubinin110 , M. Dubovsky28a , A. Dubreuil54 , E. Duchovni178 , G. Duckeck113 , O. A. Ducu36 ,27b ,
D. Duda114 , A. Dudarev36 , A. C. Dudder99 , M. D’uffizi100 , L. Duflot64 , M. Dührssen36 ,C.Dülsen
180 ,
M. Dumancic178 , A. E. Dumitriu27b , M. Dunford61a , S. Dungs47 , A. Duperrin101 , H. Duran Yildiz4a ,
M. Düren56 , A. Durglishvili158b , B. Dutta46 , D. Duvnjak1, G. I. Dyckes135 , M. Dyndal36 , S. Dysch100 ,
B. S. Dziedzic84 , B. Eckerova28a , M. G. Eggleston49, L.F.Ehrke
54 ,T.Eifert
8, G. Eigen17 , K. Einsweiler18 ,
T. Ekelof170 , H. El Jarrari35f , A. El Moussaouy35a , V. Ellajosyula170 , M. Ellert170 , F. Ellinghaus180 ,
A. A. Elliot92 , N. Ellis36 , J. Elmsheuser29 , M. Elsing36 , D. Emeliyanov142 ,A.Emerman
39 ,
Y. Enari162 , J. Erdmann47 , A. Ereditato20 , P. A. Erland84 , M. Errenst180 , M. Escalier64 , C. Escobar172 ,
O. Estrada Pastor172 , E. Etzion160 , G. Evans138a , H. Evans65 , M.O.Evans
155 , A. Ezhilov136 , F. Fabbri57 ,
L. Fabbri23b,23a , V. Fabiani118 , G. Facini176 , R. M. Fakhrutdinov122, S. Falciano72a , P.J.Falke
24 ,
S. Falke36 , J. Faltova141 , Y. Fang15a , Y. Fang15a , G. Fanourakis44 , M. Fanti68a,68b , M. Faraj60c ,
A. Farbin8, A. Farilla74a ,E.M.Farina
70a,70b , T. Farooque106 , S. M. Farrington50 , P. Farthouat36 ,
F. Fassi35f , D. Fassouliotis9, M. Faucci Giannelli73a,73b , W.J.Fawcett
32 , L. Fayard64 , O. L. Fedin136 ,p,
A. Fehr20 , M. Feickert171 , L. Feligioni101 , A. Fell148 , C. Feng60b , M. Feng49 , M. J. Fenton169 ,
A. B. Fenyuk122, S. W. Ferguson43 , J. Ferrando46 , A. Ferrari170 , P. Ferrari119 , R. Ferrari70a , D. Ferrere54 ,
C. Ferretti105 , F. Fiedler99 , A. Filipˇciˇc91 , F. Filthaut118 , K. D. Finelli25 , M.C.N.Fiolhais
138a,138c,a,
L. Fiorini172 , F. Fischer113 , J. Fischer99 , W. C. Fisher106 , T. Fitschen21 , I. Fleck150 , P. Fleischmann105 ,
T. Flick180 , B. M. Flierl113 ,L.Flores
135 , L. R. Flores Castillo62a , F. M. Follega75a,75b ,N.Fomin
17 ,
J. H. Foo165 , G. T. Forcolin75a,75b , B. C. Forland65, A. Formica143 , F. A. Förster14 ,A.C.Forti
100 ,
E. Fortin101, M.G.Foti
133 , D. Fournier64 ,H.Fox
89 , P. Francavilla71a,71b , S. Francescato72a,72b ,
M. Franchini23b,23a , S. Franchino61a , D. Francis36 , L. Franco5, L. Franconi20 , M. Franklin59 ,
G. Frattari72a,72b , P. M. Freeman21, B. Freund109 , W. S. Freund80b , E. M. Freundlich47 , D. C. Frizzell127 ,
D. Froidevaux36 , J.A.Frost
133 ,Y.Fu
60a , M. Fujimoto125 , E. Fullana Torregrosa172 , T. Fusayasu115,
J. Fuster172 , A. Gabrielli23b,23a , A. Gabrielli36 , P. Gadow114 , G. Gagliardi55b,55a , L. G. Gagnon109 ,
G. E. Gallardo133 , E.J.Gallas
133 , B.J.Gallop
142 , R. Gamboa Goni92 ,K.K.Gan
126 , S. Ganguly178 ,
J. Gao60a ,Y.Gao
50 ,Y.S.Gao
31,m, F. M. Garay Walls145a , C. García172 , J. E. García Navarro172 ,
J. A. García Pascual15a , M. Garcia-Sciveres18 , R. W. Gardner37 , S. Gargiulo52 , C. A. Garner165, V. Garonne132 ,
S. J. Gasiorowski147 , P. Gaspar80b , G. Gaudio70a , P. Gauzzi72a,72b , I. L. Gavrilenko110 , A. Gavrilyuk123 ,
C. Gay173 , G. Gaycken46 , E. N. Gazis10 , A. A. Geanta27b , C.M.Gee
144 , C.N.P.Gee
142 ,J.Geisen
96 ,
M. Geisen99 , C. Gemme55b , M. H. Genest58 , C. Geng105, S. Gentile72a,72b , S. George93 , T. Geralis44 ,
L. O. Gerlach53, P. Gessinger-Befurt99 , G. Gessner47 , M. Ghasemi Bostanabad174 , M. Ghneimat150 ,
A. Ghosh169 , A. Ghosh77 , B. Giacobbe23b , S. Giagu72a,72b , N. Giangiacomi165 , P. Giannetti71a ,
A. Giannini69a,69b ,S.M.Gibson
93 , M. Gignac144 ,D.T.Gil
83b , B. J. Gilbert39 , D. Gillberg34 , G. Gilles180 ,
N. E. K. Gillwald46 , D. M. Gingrich3,ak , M. P. Giordani66a,66c , P. F. Giraud143 , G. Giugliarelli66a,66c ,
D. Giugni68a , F. Giuli73a,73b , S. Gkaitatzis161 , I. Gkialas9,h, E. L. Gkougkousis14 , P. Gkountoumis10 ,
L. K. Gladilin112 ,C.Glasman
98 , G. R. Gledhill130 , I. Gnesi41b ,c, M. Goblirsch-Kolb26 , D. Godin109 ,
S. Goldfarb104 , T. Golling54 , D. Golubkov122 , A. Gomes138a,138b , R. Goncalves Gama53 , R. Gonçalo138a ,138c ,
G. Gonella130 , L. Gonella21 , A. Gongadze79 , F. Gonnella21 , J. L. Gonski39 , S. González de la Hoz172 ,
S. Gonzalez Fernandez14 , R. Gonzalez Lopez90 , C. Gonzalez Renteria18 , R. Gonzalez Suarez170 ,
S. Gonzalez-Sevilla54 , G. R. Gonzalvo Rodriguez172 , L. Goossens36 , N.A.Gorasia
21 , P. A. Gorbounov123 ,
H. A. Gordon29 ,B.Gorini
36 ,E.Gorini
67a,67b , A. Gorišek91 , A. T. Goshaw49 , M. I. Gostkin79 ,
C. A. Gottardo118 , M. Gouighri35b , A. G. Goussiou147 , N. Govender33c ,C.Goy
5, I. Grabowska-Bold83a ,
E. Gramstad132 , S. Grancagnolo19 , M. Grandi155 , V. Gratchev136, P.M.Gravila
27f , F. G. Gravili67a ,67b ,
C. Gray57 ,H.M.Gray
18 , C. Grefe24 , I. M. Gregor46 , P. Grenier152 ,K.Grevtsov
46 , C. Grieco14 ,
N. A. Grieser127, A. A. Grillo144 ,K.Grimm
31,l, S. Grinstein14 ,w,J.-F.Grivaz
64 ,S.Groh
99 ,E.Gross
178 ,
J. Grosse-Knetter53 , Z.J.Grout
94 ,C.Grud
105, A. Grummer117 , J. C. Grundy133 , L. Guan105 , W. Guan179 ,
C. Gubbels173 , J. Guenther36 , J. G. R. Guerrero Rojas172 , F. Guescini114 , D. Guest19 , R. Gugel99 ,
A. Guida46 , T. Guillemin5, S. Guindon36 ,J.Guo
60c ,L.Guo
64 ,Y.Guo
105 ,Z.Guo
101 , R. Gupta46 ,
S. Gurbuz24 , G. Gustavino127 ,M.Guth
52 , P. Gutierrez127 , L. F. Gutierrez Zagazeta135 , C. Gutschow94 ,
C. Guyot143 , C. Gwenlan133 , C. B. Gwilliam90 , E. S. Haaland132 , A. Haas124 , M. H. Habedank19 ,
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C. Haber18 , H. K. Hadavand8, A. Hadef99 , M. Haleem175 ,J.Haley
128 , J.J.Hall
148 , G. Halladjian106 ,
G. D. Hallewell101 , K. Hamano174 , H. Hamdaoui35f , M. Hamer24 , G.N.Hamity
50 ,K.Han
60a ,L.Han
15c ,
L. Han60a ,S.Han
18 , Y.F.Han
165 , K. Hanagaki81,u, M. Hance144 , M. D. Hank37 , R. Hankache100 ,
E. Hansen96 , J. B. Hansen40 , J. D. Hansen40 , M. C. Hansen24 , P. H. Hansen40 , E. C. Hanson100 ,
K. Hara167 , T. Harenberg180 , S. Harkusha107 , P. F. Harrison176, N.M.Hartman
152 , N. M. Hartmann113 ,
Y. Ha se g aw a149 ,A.Hasib
50 , S. Hassani143 , S. Haug20 , R. Hauser106 , M. Havranek140 , C.M.Hawkes
21 ,
R. J. Hawkings36 , S. Hayashida116 , D. Hayden106 , C. Hayes105 , R. L. Hayes173 , C. P. Hays133 ,
J. M. Hays92 , H. S. Hayward90 , S. J. Haywood142 ,F.He
60a ,Y.He
163 ,Y.He
134 , M. P. Heath50 ,
V. Hedberg96 , A. L. Heggelund132 , N.D.Hehir
92 , C. Heidegger52 , K. K. Heidegger52 , W. D. Heidorn78 ,
J. Heilman34 ,S.Heim
46 ,T.Heim
18 , B. Heinemann46,ai , J. G. Heinlein135 ,J.J.Heinrich
130 , L. Heinrich36 ,
J. Hejbal139 , L. Helary46 ,A.Held
124 , S. Hellesund132 , C. M. Helling144 , S. Hellman45a,