Available via license: CC BY 4.0
Content may be subject to copyright.
First Measurement of Charged Current Muon Neutrino-Induced K+Production on
Argon using the MicroBooNE Detector
P. Abratenko,39 D. Andrade Aldana,14 L. Arellano,22 J. Asaadi,38 A. Ashkenazi,37 S. Balasubramanian,12
B. Baller,12 A. Barnard,29 G. Barr,29 D. Barrow,29 J. Barrow,26 V. Basque,12 J. Bateman,15, 22
O. Benevides Rodrigues,14 S. Berkman,25 A. Bhat,7M. Bhattacharya,12 M. Bishai,3A. Blake,19 B. Bogart,24
T. Bolton,18 M. B. Brunetti,17, 41 L. Camilleri,10 D. Caratelli,4F. Cavanna,12 G. Cerati,12 A. Chappell,41
Y. Chen,33 J. M. Conrad,23 M. Convery,33 L. Cooper-Troendle,30 J. I. Crespo-Anad´on,6R. Cross,41 M. Del Tutto,12
S. R. Dennis,5P. Detje,5R. Diurba,2Z. Djurcic,1K. Duffy,29 S. Dytman,30 B. Eberly,35 P. Englezos,32
A. Ereditato,7, 12 J. J. Evans,22 C. Fang,4G. A. Fiorentini Aguirre,34 W. Foreman,14, 20 B. T. Fleming,7D. Franco,7
A. P. Furmanski,26 F. Gao,4D. Garcia-Gamez,13 S. Gardiner,12 G. Ge,10 S. Gollapinni,20 E. Gramellini,22
P. Green,29 H. Greenlee,12 L. Gu,19 W. Gu,3R. Guenette,22 P. Guzowski,22 L. Hagaman,7M. D. Handley,5
O. Hen,23 C. Hilgenberg,26 G. A. Horton-Smith,18 A. Hussain,18 B. Irwin,26 M. S. Ismail,30 C. James,12 X. Ji,27
J. H. Jo,3R. A. Johnson,8D. Kalra,10 G. Karagiorgi,10 W. Ketchum,12 M. Kirby,3T. Kobilarcik,12 N. Lane,15, 22
J.-Y. Li,11 Y. Li,3K. Lin,32 B. R. Littlejohn,14 L. Liu,12 W. C. Louis,20 X. Luo,4T. Mahmud,19 C. Mariani,40
D. Marsden,22 J. Marshall,41 N. Martinez,18 D. A. Martinez Caicedo,34 S. Martynenko,3A. Mastbaum,32
I. Mawby,19 N. McConkey,31 V. Meddage,18 L. Mellet,25 J. Mendez,21 J. Micallef,23, 39 A. Mogan,9T. Mohayai,16
M. Mooney,9A. F. Moor,5C. D. Moore,12 L. Mora Lepin,22 M. M. Moudgalya,22 S. Mulleriababu,2D. Naples,30
A. Navrer-Agasson,15 N. Nayak,3M. Nebot-Guinot,11 C. Nguyen,32 J. Nowak,19 N. Oza,10 O. Palamara,12
N. Pallat,26 V. Paolone,30 A. Papadopoulou,1V. Papavassiliou,28 H. B. Parkinson,11 S. F. Pate,28 N. Patel,19
Z. Pavlovic,12 E. Piasetzky,37 K. Pletcher,25 I. Pophale,19 X. Qian,3J. L. Raaf,12 V. Radeka,3A. Rafique,1
M. Reggiani-Guzzo,11 J. Rodriguez Rondon,34 M. Rosenberg,39 M. Ross-Lonergan,20 I. Safa,10 D. W. Schmitz,7
A. Schukraft,12 W. Seligman,10 M. H. Shaevitz,10 R. Sharankova,12 J. Shi,5E. L. Snider,12 M. Soderberg,36
S. S¨oldner-Rembold,15 J. Spitz,24 M. Stancari,12 J. St. John,12 T. Strauss,12 A. M. Szelc,11 N. Taniuchi,5K. Terao,33
C. Thorpe,22 D. Torbunov,3D. Totani,4M. Toups,12 A. Trettin,22 Y.-T. Tsai,33 J. Tyler,18 M. A. Uchida,5
T. Usher,33 B. Viren,3J. Wang,27 M. Weber,2H. Wei,21 A. J. White,7S. Wolbers,12 T. Wongjirad,39 K. Wresilo,5
W. Wu,30 E. Yandel,4, 20 T. Yang,12 L. E. Yates,12 H. W. Yu,3G. P. Zeller,12 J. Zennamo,12 and C. Zhang3
(The MicroBooNE Collaboration)∗
1Argonne National Laboratory (ANL), Lemont, IL, 60439, USA
2Universit¨at Bern, Bern CH-3012, Switzerland
3Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA
4University of California, Santa Barbara, CA, 93106, USA
5University of Cambridge, Cambridge CB3 0HE, United Kingdom
6Centro de Investigaciones Energ´eticas, Medioambientales y Tecnol´ogicas (CIEMAT), Madrid E-28040, Spain
7University of Chicago, Chicago, IL, 60637, USA
8University of Cincinnati, Cincinnati, OH, 45221, USA
9Colorado State University, Fort Collins, CO, 80523, USA
10Columbia University, New York, NY, 10027, USA
11University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
12Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA
13Universidad de Granada, Granada E-18071, Spain
14Illinois Institute of Technology (IIT), Chicago, IL 60616, USA
15Imperial College London, London SW7 2AZ, United Kingdom
16Indiana University, Bloomington, IN 47405, USA
17The University of Kansas, Lawrence, KS, 66045, USA
18Kansas State University (KSU), Manhattan, KS, 66506, USA
19Lancaster University, Lancaster LA1 4YW, United Kingdom
20Los Alamos National Laboratory (LANL), Los Alamos, NM, 87545, USA
21Louisiana State University, Baton Rouge, LA, 70803, USA
22The University of Manchester, Manchester M13 9PL, United Kingdom
23Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA
24University of Michigan, Ann Arbor, MI, 48109, USA
25Michigan State University, East Lansing, MI 48824, USA
26University of Minnesota, Minneapolis, MN, 55455, USA
27Nankai University, Nankai District, Tianjin 300071, China
28New Mexico State University (NMSU), Las Cruces, NM, 88003, USA
29University of Oxford, Oxford OX1 3RH, United Kingdom
30University of Pittsburgh, Pittsburgh, PA, 15260, USA
arXiv:2503.00291v1 [hep-ex] 1 Mar 2025
2
31Queen Mary University of London, London E1 4NS, United Kingdom
32Rutgers University, Piscataway, NJ, 08854, USA
33SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
34South Dakota School of Mines and Technology (SDSMT), Rapid City, SD, 57701, USA
35University of Southern Maine, Portland, ME, 04104, USA
36Syracuse University, Syracuse, NY, 13244, USA
37Tel Aviv University, Tel Aviv, Israel, 69978
38University of Texas, Arlington, TX, 76019, USA
39Tufts University, Medford, MA, 02155, USA
40Center for Neutrino Physics, Virginia Tech, Blacksburg, VA, 24061, USA
41University of Warwick, Coventry CV4 7AL, United Kingdom
The MicroBooNE experiment is an 85 tonne active mass liquid argon time projection chamber
neutrino detector exposed to the on-axis Booster Neutrino Beam (BNB) at Fermilab. One of
MicroBooNE’s physics goals is the precise measurement of neutrino interactions on argon in the
1 GeV energy regime. Building on the capabilities of the MicroBooNE detector, this analysis
identifies K+mesons, a key signature for the study of strange particle production in neutrino
interactions. This measurement is furthermore valuable for background estimation for future nucleon
decay searches and for improved reconstruction and particle identification capabilities in experiments
such as the Deep Underground Neutrino Experiment (DUNE). In this letter, we present the first-
ever measurement of a flux-integrated cross section for charged-current muon neutrino induced
K+production on argon nuclei, determined to be 7.93 ±3.27 (stat.) ±2.92 (syst.) ×10−42
cm2/nucleon based on an analysis of 6.88×1020 protons on target.
Within the Standard Model (SM), the stability of
protons is a consequence of the global symmetry that
conserves baryon number. However, various Grand
Unified Theories (GUTs) predicting baryon number vi-
olation through nucleon decay suggest different decay
channels, with a preference for p→e+π0, with lifetimes
ranging from 1034 to 1039 years [1]. Similarly, the
Supersymmetric Grand Unified Theory (SUSY GUT)
favors protons decaying via p→νK +, with lifetimes of
1032 to 1035 years [2].
Upcoming large long-baseline neutrino oscillation ex-
periments, such as the Deep Underground Neutrino
Experiment (DUNE) [3] and Hyper-Kamiokande [4], have
the sensitivity to search for proton decay in this range.
Cherenkov detector experiments like Super-Kamiokande
[5–7] and Hyper-Kamiokande are particularly suited for
p→e+π0decay searches. However, for water Cherenkov
detectors, the p→νK +decay channel needs to be indi-
rectly detected since the momentum of the produced K+
(339 MeV/c) is below the Cherenkov detection threshold
in water (560 MeV/c). Furthermore, backgrounds from
atmospheric neutrino interactions, such as the νµn→
µ−K+nand νp →ν K+Λ channels, are challenging to
distinguish in water Cherenkov detectors.
In contrast, liquid argon time projection chambers
(LArTPCs) can directly measure kaons that range out
and decay by analyzing their unique energy loss (dE/dx)
profile. These profiles, along with topological signatures
such as distinctive kinks from kaon decay and the high
ionization Bragg peak, allow an enhanced kaon identifica-
tion. All of these topological and calorimetric features are
easily measured using the millimiter-precision imaging
capabilities of a LArTPC.
∗microboone info@fnal.gov
The first measurements of charged-current (CC) and
neutral-current (NC) kaon production, reported with the
Argonne National Laboratory [8] and GARGAMELLE
bubble chambers [9] in 1974 and 1975, observed O(10)
events. Later, in the 1980’s, the Brookhaven National
Laboratory bubble chamber reported O(10) CC and
NC events with strange particle production in neutrino-
hydrogen and neutrino-deuterium interactions [10, 11],
and the Fermilab bubble chamber reported O(10) events
and a measured cross section for CC neutrino interactions
νµ+n−→ µ−+Λ+K+on a deuterium target for energy
ranges between 10 GeV and 250 GeV [12]. Most recently,
the MINERνA experiment measured K+production in
NC and CC modes on a hydrocarbon target, reporting
O(100) and O(1000) events respectively [13–15]. In ad-
dition, MINERνA reported the first evidence of coherent
kaon production [15, 16].
This letter presents the first measurement of the cross
section for K+production in neutrino interactions on
argon nuclei. The signature of the signal events is a CC
νµinteraction in the presence of a K+in the final state
with momentum 230 MeV/c < pK+<2900 MeV/c.
The K+should decay either to µ+νµor π+π0which
have branching ratios of 63.56% and 20.67%, respectively
[17]. This measurement uses MicroBooNE datasets
collected between 2016 and 2018 from the on-axis Booster
Neutrino Beam (BNB), equivalent to 6.88×1020 protons
on target (POT).
As the first quantitative measurement of K+produc-
tion on argon nuclei, this analysis will impact nuclear,
neutrino, and proton decay research on two fronts. First,
it provides background constraints for future nucleon
decay experiments. Atmospheric neutrinos can also
produce a K+along with other particles, mimicking the
signature of p→νK +decay. Thus, neutrino-induced
K+production serves as a background for proton decay
3
studies. Second, this measurement provides input to
improve neutrino generator models. In the case of bound
proton decay, the K+is generated within the nucleus and
may interact with other nucleons as it escapes. These
interactions can decrease the detected momentum of the
K+in the detector and may also eject nucleons from the
nucleus.
Charged-current neutrino-induced K+production oc-
curs through three interaction modes: (1) single kaon
production, where a CC interaction produces a single
kaon with a production energy threshold of Eνµ≥0.79
GeV, and strangeness is not conserved (|∆S|= 1); (2)
associated kaon production, where both a kaon and a
hyperon are produced, with a threshold of Eνµ≥1.2
GeV, and strangeness is conserved; and (3) coherent
production, where only one kaon is produced in the final
state, and the target nucleus remains intact after the
neutrino interaction.
Figure 1 shows an event candidate of CC νµkaon
production recorded in MicroBooNE data. The kaon
decays at rest into a 388 MeV energy muon, which
subsequently decays into a Michel electron.
FIG. 1. A νµ+Ar →µ−+K+interaction candidate observed
in MicroBooNE data recorded by the collection plane. The
color scale represents the intensity of the ionization charge
collected on the TPC wires (green: low intensity, red: high
intensity). The x and y-axes represent the wire position in
the beam direction and the distance in the drift direction,
respectively. The gap shown on each track is due to a region
in the detector with no active wires.
The MicroBooNE detector [18, 19] consists of an 85-
tonne LArTPC and a photon detection system comprised
of 32 photomultiplier tubes (PMTs). The detector iden-
tifies neutrino interactions by detecting ionization and
scintillation light produced by charged particles moving
through the detector. Ionization charge is recorded
across three wire planes (two induction, one collection)
oriented at different angles (with induction planes at
±60◦relative to the vertical collection plane). This
configuration allows the generation of high-resolution,
three-dimensional images of neutrino interactions. Scin-
tillation light collected by the PMTs provides precise
timing information, essential for identifying neutrino
interactions that are synchronized with the BNB, and
for rejecting cosmic-ray backgrounds.
In MicroBooNE, the LArSoft framework [20] pro-
vides tools for simulating the neutrino flux, neutrino
interactions, particle propagation, and detector response
including ionization and scintillation processes as well
as reconstruction of neutrino events. The BNB neu-
trino flux through the MicroBooNE detector is simu-
lated using the MiniBooNE flux model [21] adapted for
MicroBooNE’s location along the beamline. GENIE
v3.0.6 (G18 10a 02 11a) [22, 23] is the event generator
used to simulate neutrino-argon interactions, includ-
ing both single and associated kaon production. In
GENIE, associated kaons are produced via individual
resonances simulated by the Rein-Sehgal model [24].
Kaons produced via Deep Inelastic Scattering (DIS) are
simulated by the Koba-Nielsen-Olesen (KNO) scaling
parameterization [25]. For higher energies (hadronic
invariant mass W>2.3 GeV), the AGKY model [26]
and PYTHIA6 [27] are used. The hadronization models
are tuned to match strange particle production (Λ and
K0
S) observed in experiments such as the Big European
Bubble Chamber (BEBC) [28–31] and the Fermilab
bubble chamber [32, 33]. The single kaon production
model [34] is implemented in GENIE, generating events
in the channels νl+n→l−+n+K+,νl+p→l−+p+K+,
and νl+n→l−+p+K0. The region of validity assumed
for the model is for Eν≤2 GeV [34, 35], while the GENIE
authors note that the model can be applied to higher
energies as well [36].
Particle trajectories and interactions within the de-
tector are simulated using GEANT4 [37], which models
particle propagation in the detector. Simulated neutrino
interactions are overlayed with data events collected us-
ing an unbiased trigger that operates in anti-coincidence
with the beam, enabling data-driven modeling of cosmic
ray and detector noise. Both simulation and data
reconstruction use the Pandora framework [38], which
uses a multi-algorithm approach to pattern recognition
and event reconstruction in LArTPCs. Pandora creates
clusters from wire hits, identifies the neutrino vertex,
matches clusters across wire planes to form 3D objects,
classifies reconstructed particles as tracks or showers, and
establishes particle hierarchies.
Before performing a dedicated selection for K+pro-
duction signal events, a pre-selection is applied as follows:
(1) events must pass a CC inclusive filter which isolates
CC neutrino interactions by identifying the presence
of an outgoing muon from the vertex of a neutrino
interaction [39], (2) all reconstructed tracks in an event
must originate within a 2.4 m ×2.1 m ×9.8 m fiducial
volume centered inside the MicroBooNE TPC, and (3)
the daughter track (defined as a track connected to the
endpoint of one primary signal track candidate) should
stop at least 5 cm away from any of the physical edges
of the TPC to ensure containment.
For the K+event selection, we implemented a boosted
4
decision tree (BDT) classifier [40]. The BDT classifier
inputs include reconstructed variables such as χ2values
which compare the expected dE/dx for kaon (χ2
k), proton
(χ2
p), pion (χ2
π), and muon (χ2
µ) hypotheses with the
observed data for each wire plane, the combined 3-
wire-plane χ2, the daughter track length, and the log-
likelihood ratio particle identification (LLR PID) [41].
We trained the BDT classifier to identify true K+that
decay to µ+νµor π+π0, using Monte Carlo (MC) simula-
tion samples of reconstructed neutrino interactions that
meet the preselection criteria described above. To train
the BDT classifier, a background sample corresponding
to 3.67 ×1020 POT (equivalent to 10% of the Micro-
BooNE BNB simulation) was used. For the simulation
of the signal, a dedicated high-statistics sample of single
and associated K+production was used. We optimized
the BDT classifier score selection criteria by maximizing
the product of efficiency and purity. Figure 2 shows the
BDT classifier score distribution, with the orange arrow
at 0.41 representing the selected BDT score cut (BDT
selection). Events with a score higher than the BDT
selection are kept as K+candidates, and 10 such K+
candidates are identified in the data. The total number of
background events predicted from the overlay simulation
and off-beam data is 2.21 events. The simulation predicts
a selection efficiency of 3.95%, and an associated selection
purity of 89%. The primary background category for K+
candidates consists of protons generated at the neutrino
vertex that interact with an argon nucleus, producing
secondary particles at the proton endpoint that could
mimic a K+decay product, consisting of 2.08 events.
FIG. 2. BDT classifier score distribution. The black dots
represent the MicroBooNE data. The stacked histogram
represents the expected BDT distribution from the simulation
with its statistical uncertainty (gray band). The orange arrow
represents the BDT event selection cut. The red triangles are
the K+candidates selected in MicroBooNE data by the BDT.
FIG. 3. Reconstructed daughter track length distribution.
The stacked histogram shows the MC simulation with its
statistical uncertainty (gray band). The black dots represent
the MicroBooNE data. The red triangles are the K+
candidates selected in MicroBooNE data by the BDT.
Figure 3 shows the reconstructed daughter track length
distribution of the K+candidates selected in Micro-
BooNE data, including the event shown in Fig. 1.
The stacked histogram shows the expected daughter
track length distribution from the MicroBooNE BNB
simulation. Due to the two-body decay nature of
the K+branching ratios analyzed in this letter, the
resulting mono-energetic µ+(π+) provides a powerful
discriminant, as it leads to a fixed track length that can
be measured with high accuracy and is not present in
background events. The expected track length for µ+and
π+accumulates around 53 cm and 30 cm, respectively.
The K+candidates selected by the BDT selection in Fig.
3 (red triangles) exhibit a pile-up in the region compatible
with K+decay.
To evaluate the accuracy of the background modeling,
two sideband regions were defined using a 2-dimensional
distribution of the BDT classifier score versus daughter
track length. A far sideband region is defined to include
tracks with low BDT classifier scores (BDT <0) and
track lengths less than 40 cm. A near sideband region se-
lects tracks with BDT classifier scores close to the signal
region (BDT <0.41) and across all track length ranges,
but excludes tracks in the far sideband region. The
data/MC comparisons of the near and far sidebands show
agreement within uncertainties for the reconstructed
kinematic variables such as K+candidate track length,
ϕ(azimuthal angle around the beam direction), and θ
(angle of the track with respect to the beam direction).
The agreement with the data suggests that the simulation
is able to adequately model the background in the cross
section extraction. More information about the sideband
5
comparisons is provided in the Supplemental Material
[42].
This analysis presents the measurement of the single-
bin flux-integrated cross section for νµ+ Ar →µ−+
K++Xinteractions, where muon neutrinos interact
with Ar and produce one µ−, one K+and X(any
number of other hadrons in the final state) on argon
nuclei. This measurement uses an exposure of 6.88×1020
POT from the on-axis BNB neutrino beam. The cross
section (σ) was found to be 7.93 ±3.27 (stat.) ±2.92
(syst.) 10−42cm2/nucleon, and it was calculated using
the following expression:
σ=N−B
ε×Ntarget ×Φνµ
, (1)
where Nrepresents the number of selected signal events
(10), Bis the number of expected background events
(2.21), εis the selection efficiency (3.95%), Ntarget is the
number of target nucleons (4.13 ×1031), and Φνµstands
for the integrated BNB νµflux (6.02 ×1011ν/cm2). The
selection efficiency accounts for all possible K+decays
within the fiducial volume, while the BDT selection
identifies K+candidates that could decay into either µ+
or π+.
The systematic uncertainties in the measured cross
section are evaluated by reweighting and generating
simulated events to account for each source of uncer-
tainty [43–45]. For the detector-related uncertainties,
a dedicated simulation is used to model the variation
in detector responses. In contrast, uncertainties from
neutrino interactions and BNB flux modeling are treated
with a similar reweighting technique. To evaluate the
uncertainty associated with kaon reinteractions within
the detector, we implement a methodology similar to
that presented by MINERνA [13]. For each selected
K+candidate track, a weight Winel is calculated using
the following expression to estimate the kaon inelastic
reinteraction uncertainty:
Winel =1−e−ρxσtot
var
1−e−ρxσtot
geant
×σinel
var
σtot
var
×σtot
geant
σinel
geant
, (2)
where ρis the density of the material, xis the track
length, σtot
geant and σinel
geant are the K+total and inelastic
GEANT4 cross sections, and σtot
var and σinel
var are the K+
total and inelastic cross sections estimated by applying
a variation of ±40% to the σinel
geant. The 40% variation
covers the disagreement between data and GEANT4
in K+−Ar interaction measurements reported by the
LArIAT experiment [46], as well as the disagreement
between K+−Ca interaction data and the GEANT4
interaction model [42].
The leading source of systematic uncertainty comes
from the detector response (30.3%), with a major con-
tribution from modeling ion recombination of electrons
with argon. This analysis relies heavily on identifying
the stopping K+and µ+(π+) signatures through their
Bragg peak, which itself depends on the charge profile of
the track in a highly ionizing regime. Because of this,
the mismodeling of ion recombination, which alters the
calorimetry in the detector, plays a particularly large
role in the total error budget for the measurement.
The modeling of neutrino interactions, the BNB neu-
trino flux, and particle reinteractions in the detector,
including kaon inelastic interactions, lead to systematic
uncertainties of 12.1%, 11.7%, and 10.4% respectively.
The effect of statistics in the estimation of the selection
efficiency (6.1%), the number of target nuclei Ntarget
(1%), simulation statistical uncertainty (7.4%), and POT
exposure (2%) are the remaining sources of uncertainty.
The overall magnitude of the uncertainty (statistical and
systematic) on our measurement is 55.3%, where the
major contribution is due to the statistical uncertainty,
with a reported value of 41.3%. Table 1 presents the
measured cross section of the νµ+ Ar →µ−+K++X
interactions and the predictions obtained from various
neutrino event generators.
Generator cross section (10−42cm2/nucleon)
GENIE v2.12.10 8.67
GENIE v3.00.06 8.42
NEUT 5.4.0.1 9.71
NuWro 19.02.1 10.87
MicroBooNE Data 7.93 ±3.27 (stat.) ±2.92 (syst.)
TABLE I. The νµ+Ar →µ−+K++Xcross section extracted
from different neutrino event generators compared with the
cross section extracted from data.
The flux-integrated cross section measurement re-
ported in this paper indicates consistency with the
predictions from different neutrino event generators,
although the large uncertainties prevent a stringent test
of these models. The cross section predicted by the
NuWro generator [47] is the largest among the generator
predictions, but is still within the 1σuncertainty of the
data result.
In conclusion, this letter presents the first measure-
ment of the νµ+ Ar →µ−+K++Xcross section
on argon, using the MicroBooNE dataset collected from
the BNB flux with a POT of 6.88 ×1020. The results
reported here represent a significant step forward for
future studies aiming to refine background estimates for
proton decay searches, particularly within the framework
of GUTs and SUSY. To advance this analysis in fu-
ture LArTPC measurements, it is necessary to increase
statistics, reduce large detector systematics (especially
those related to the recombination of electrons with
argon nuclei), refine reconstruction algorithms for short
tracks, and improve the identification of kaon tracks that
undergo reinteractions. Furthermore, the uncertainty
related to kaon reinteractions on argon nuclei could be
refined by using data from LArIAT [46], and the DUNE
prototypes at CERN [48]. More statistics will come
from analyzing MicroBooNE’s full dataset with a total
6
of 1.2 ×1021 POT. The future DUNE near detector
data could also provide more accurate measurements
of CC neutrino-induced K+production on argon nuclei
enabling improved comparisons among the neutrino gen-
erator models. These high-precision measurements will
enhance our understanding of rare neutrino interactions
and final state interaction modeling, and will improve the
background predictions for future nucleon decay searches
at DUNE, which currently rely on model predictions.
This document was prepared by the MicroBooNE
collaboration using the resources of the Fermi National
Accelerator Laboratory (Fermilab), a U.S. Department of
Energy, Office of Science, Office of High Energy Physics
HEP User Facility. Fermilab is managed by Fermi
Forward Discovery Group, LLC, acting under Contract
No. 89243024CSC000002. MicroBooNE is supported by
the following: the U.S. Department of Energy, Office
of Science, Offices of High Energy Physics and Nuclear
Physics; the U.S. National Science Foundation; the
Swiss National Science Foundation; the Science and
Technology Facilities Council (STFC), part of the United
Kingdom Research and Innovation; the Royal Society
(United Kingdom); the UK Research and Innovation
(UKRI) Future Leaders Fellowship; and the NSF AI
Institute for Artificial Intelligence and Fundamental In-
teractions. Additional support for the laser calibration
system and cosmic ray tagger was provided by the
Albert Einstein Center for Fundamental Physics, Bern,
Switzerland. We also acknowledge the contributions of
technical and scientific staff to the design, construction,
and operation of the MicroBooNE detector as well as the
contributions of past collaborators to the development of
MicroBooNE analyses, without whom this work would
not have been possible. For the purpose of open
access, the authors have applied a Creative Commons
Attribution (CC BY) public copyright license to any
Author Accepted Manuscript version arising from this
submission.
[1] H. Georgi and S. L. Glashow, “Unity of All Elementary-
Particle Forces,” Phys. Rev. Lett. 32, 438–441 (1974).
[2] J. Hisano, H. Murayama, and T. Yanagida, “Nucleon
decay in the minimal supersymmetric SU(5) grand uni-
fication,” Nucl. Phys. B 402, 46–84 (1993), arXiv:hep-
ph/9207279.
[3] B. Abi et al., “Deep Underground Neutrino Experiment
(DUNE), Far Detector Technical Design Report, Volume
I. Introduction to DUNE,” J. Instrum. 15, T08008
(2020).
[4] K. Abe et al. (Super-Kamiokande Collaboration),
“Hyper-Kamiokande Design Report,” (2018),
arXiv:1805.04163 [physics.ins-det].
[5] K. Abe et al. (Super-Kamiokande Collaboration),
“Search for proton decay via p→νK+using
260 kiloton ·year data of Super-Kamiokande,” Phys.
Rev. D 90, 072005 (2014).
[6] K. Abe et al. (Super-Kamiokande Collaboration),
“Search for proton decay via p→e+π0and p→µ+π0in
0.31 megaton ·years exposure of the Super-Kamiokande
water Cherenkov detector,” Phys. Rev. D 95, 012004
(2017).
[7] K. Abe et al. (Super-Kamiokande Collaboration),
“Search for proton decay via p→e+π0and p→µ+π0
with an enlarged fiducial volume in Super-Kamiokande
I-IV,” Phys. Rev. D 102, 112011 (2020).
[8] S. J. Barish et al., “Strange-Particle Production in
Neutrino Interactions,” Phys. Rev. Lett. 33, 1446–1449
(1974).
[9] H Deden et al., “Strange particle production and
charmed particle search in the Gargamelle neutrino
experiment,” Phys. Lett., B, v. 58, no. 3, pp. 361-366
(1975), 10.1016/0370-2693(75)90674-7.
[10] J. E. Jensen, “THE BROOKHAVEN NATIONAL LAB-
ORATORY 80-INCH LIQUID HYDROGEN BUBBLE
CHAMBER,” in 1964 Cryogenic Engineering Conference
(1964).
[11] N. J. Baker et al., “Strange-particle production from
neutrino interactions in the BNL 7-foot bubble chamber,”
Phys. Rev. D 24, 2779–2786 (1981).
[12] D. Son et al., “Quasielastic charmed-baryon production
and exclusive strange-particle production by high-energy
neutrinos,” Phys. Rev. D 28, 2129–2134 (1983).
[13] C. M. Marshall et al. (MINERνA Collaboration), “Mea-
surement of K+production in charged-current νµinter-
actions,” Phys. Rev. D 94, 012002 (2016).
[14] C. M. Marshall et al. (MINERνA Collaboration), “Mea-
surement of Neutral-Current K+Production by Neu-
trinos using MINERνA,” Phys. Rev. Lett. 119, 011802
(2017).
[15] C. M. Marshall, Measurement of charged kaon production
by neutrinos at MINERνA, Ph.D. thesis, U. Rochester
(2016).
[16] Z. Wang, C. M. Marshall, et al. (MINERνA Collabo-
ration), “Evidence of Coherent K+Meson Production
in Neutrino-Nucleus Scattering,” Phys. Rev. Lett. 117,
061802 (2016).
[17] S. Navas et al. (Particle Data Group Collaboration),
“Review of Particle Physics,” Phys. Rev. D 110, 030001
(2024).
[18] B. Fleming (MicroBooNE Collaboration), “The
MicroBooNE Technical Design Report,” (2012),
10.2172/1333130.
[19] R. Acciarri et al. (MicroBooNE Collaboration), “Design
and Construction of the MicroBooNE Detector,” JINST
12, P02017 (2017), arXiv:1612.05824 [physics.ins-det].
[20] E. L. Snider and G. Petrillo, “LArSoft: Toolkit for
Simulation, Reconstruction and Analysis of Liquid Argon
TPC Neutrino Detectors,” J. Phys. Conf. Ser. 898,
042057 (2017).
[21] A. A. Aguilar-Arevalo et al. (MiniBooNE Collaboration),
“Neutrino flux prediction at MiniBooNE,” Phys. Rev. D
79, 072002 (2009).
[22] C. Andreopoulos et al., “The GENIE Neutrino Monte
Carlo Generator,” Nucl. Instrum. Meth. A 614, 87–104
(2010), arXiv:0905.2517 [hep-ph].
[23] C. Andreopoulos et al. (GENIE Collaboration),
“Neutrino-nucleon cross-section model tuning in GENIE
7
v3,” Phys. Rev. D 104, 072009 (2021).
[24] D. Rein and L. M. Sehgal, “Neutrino-excitation of
baryon resonances and single pion production,” Annals
of Physics 133, 79–153 (1981).
[25] Z. Koba, H.B. Nielsen, and P. Olesen, “Scaling of mul-
tiplicity distributions in high energy hadron collisions,”
Nucl. Phys. B 40, 317–334 (1972).
[26] T. Yang, C. Andreopoulos, H. Gallagher, K. Hofmann,
and P. Kehayias, “A hadronization model for few-GeV
neutrino interactions,” Eur. Phys. J. C 63, 1–10 (2009).
[27] T. Sj¨ostrand, S. Mrenna, and P. Skands, “PYTHIA
6.4 physics and manual,” J. High Energy Phys. 2006,
026–026 (2006).
[28] G. T. Jones et al., “Neutral strange particle production in
neutrino and anti-neutrino charged current interactions
on protons,” Z. Phys. C 57, 197–210 (1993).
[29] D. Allasia et al., “Production of neutral strange particles
in νµd2and vµd2charged current interactions,” Nucl.
Phys. B 224, 1–20 (1983).
[30] S. Willocq et al. (WA59 Collaboration), “Neutral strange
particle production in anti-neutrino - neon charged cur-
rent interactions,” Z. Phys. C 53, 207–218 (1992).
[31] P. Bosetti et al., “Strange particle production in νand ν
neon interactions,” Nucl. Phys. B 209, 29–44 (1982).
[32] N. J. Baker et al., “Strange-particle production in
neutrino-neon charged-current interactions,” Phys. Rev.
D34, 1251–1264 (1986).
[33] D. DeProspo et al., “Neutral strange particle production
in neutrino and antineutrino charged-current interactions
on neon,” Phys. Rev. D 50, 6691–6703 (1994).
[34] M. Rafi Alam, I. Ruiz Simo, M. Sajjad Athar, and M. J.
Vicente Vacas, “Weak kaon production off the nucleon,”
Phys. Rev. D 82, 033001 (2010).
[35] L. Alvarez-Ruso, J. Nieves, I. Ruiz Simo, M. Valverde,
and M. J. Vicente Vacas, “Charged kaon production by
coherent scattering of neutrinos and antineutrinos on
nuclei,” Phys. Rev. C 87, 015503 (2013).
[36] M. Alam, C. Andreopoulos, et al., “GENIE Production
Release 2.10.0,” (2015), arXiv:1512.06882 [hep-ph].
[37] S. Agostinelli et al., “Geant4 — a simulation toolkit,”
Nucl. Instrum. Meth. A 506, 250–303 (2003).
[38] R. Acciarri et al., “The Pandora multi-algorithm ap-
proach to automated pattern recognition of cosmic-ray
muon and neutrino events in the MicroBooNE detector,”
Eur. Phys. J. C 78 (2018), 10.1140/epjc/s10052-017-
5481-6.
[39] P. Abratenko et al. (MicroBooNE), “First Measurement
of Inclusive Muon Neutrino Charged Current Differential
Cross Sections on Argon at Eν∼0.8 GeV with the
MicroBooNE Detector,” Phys. Rev. Lett. 123, 131801
(2019), arXiv:1905.09694 [hep-ex].
[40] A. Hoecker et al., “TMVA - Toolkit for Multivariate Data
Analysis,” (2009), arXiv:physics/0703039 [physics.data-
an].
[41] P. Abratenko et al. (MicroBooNE Collaboration),
“Calorimetric classification of track-like signatures in
liquid argon TPCs using MicroBooNE data,” J. High
Energy Phys. 2021 (2021), 10.1007/jhep12(2021)153.
[42] Supplemental Material.
[43] J. Calcutt, C. Thorpe, K. Mahn, and L. Fields,
“Geant4Reweight: a framework for evaluating and prop-
agating hadronic interaction uncertainties in Geant4,” J.
Instrum. 16, P08042 (2021).
[44] P. Abratenko et al. (MicroBooNE Collaboration), “Novel
approach for evaluating detector-related uncertainties in
a LArTPC using MicroBooNE data,” Eur. Phys. J. C 82
(2022), 10.1140/epjc/s10052-022-10270-8.
[45] The MicroBooNE Collaboration, “Neutrino Interaction
Model and Uncertainties for MicroBooNE Analyses,” Mi-
croBooNE Public Note, MICROBOONE-NOTE-1074-
PUB (2020).
[46] E Gramellini, Measurement of the Negative Pion and
Positive Kaon Total Hadronic Cross Sections on Argon
at the LArIAT Experiment, Ph.D. thesis, Yale U. (2018).
[47] T. Golan, J.T. Sobczyk, and J. ˙
Zmuda, “NuWro: the
Wroc law Monte Carlo Generator of Neutrino Interac-
tions,” Nucl. Phys. B - Proc. Suppl. 229-232, 499 (2012),
Neutrino 2010.
[48] A. Abed Abud et al. (DUNE Collaboration), “First mea-
surement of the total inelastic cross section of positively
charged kaons on argon at energies between 5.0 and 7.5
GeV,” Phys. Rev. D 110, 092011 (2024).
