The MicroBooNE Collaboration ††thanks<EMAIL_ADDRESS>
# Demonstration of neutron identification in neutrino interactions in the
MicroBooNE liquid argon time projection chamber

P. Abratenko  Tufts University, Medford, MA, 02155, USA  O. Alterkait  Tufts
University, Medford, MA, 02155, USA  D. Andrade Aldana  Illinois Institute of
Technology (IIT), Chicago, IL 60616, USA  L. Arellano  The University of
Manchester, Manchester M13 9PL, United Kingdom  J. Asaadi  University of
Texas, Arlington, TX, 76019, USA  A. Ashkenazi  Tel Aviv University, Tel Aviv,
Israel, 69978  S. Balasubramanian  Fermi National Accelerator Laboratory
(FNAL), Batavia, IL 60510, USA  B. Baller  Fermi National Accelerator
Laboratory (FNAL), Batavia, IL 60510, USA  A. Barnard  University of Oxford,
Oxford OX1 3RH, United Kingdom  G. Barr  University of Oxford, Oxford OX1 3RH,
United Kingdom  D. Barrow  University of Oxford, Oxford OX1 3RH, United
Kingdom  J. Barrow  University of Minnesota, Minneapolis, MN, 55455, USA  V.
Basque  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA
J. Bateman  The University of Manchester, Manchester M13 9PL, United Kingdom
O. Benevides Rodrigues  Illinois Institute of Technology (IIT), Chicago, IL
60616, USA  S. Berkman  Michigan State University, East Lansing, MI 48824, USA
A. Bhanderi  The University of Manchester, Manchester M13 9PL, United Kingdom
A. Bhat  University of Chicago, Chicago, IL, 60637, USA  M. Bhattacharya
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  M.
Bishai  Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  A. Blake
Lancaster University, Lancaster LA1 4YW, United Kingdom  B. Bogart  University
of Michigan, Ann Arbor, MI, 48109, USA  T. Bolton  Kansas State University
(KSU), Manhattan, KS, 66506, USA  J. Y. Book  Harvard University, Cambridge,
MA 02138, USA  M. B. Brunetti  University of Warwick, Coventry CV4 7AL, United
Kingdom  L. Camilleri  Columbia University, New York, NY, 10027, USA  Y. Cao
The University of Manchester, Manchester M13 9PL, United Kingdom  D. Caratelli
University of California, Santa Barbara, CA, 93106, USA  F. Cavanna  Fermi
National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  G. Cerati
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  A.
Chappell  University of Warwick, Coventry CV4 7AL, United Kingdom  Y. Chen
SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA  J. M. Conrad
Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA  M.
Convery  SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA  L.
Cooper-Troendle  University of Pittsburgh, Pittsburgh, PA, 15260, USA  J. I.
Crespo-Anadón  Centro de Investigaciones Energéticas, Medioambientales y
Tecnológicas (CIEMAT), Madrid E-28040, Spain  R. Cross  University of Warwick,
Coventry CV4 7AL, United Kingdom  M. Del Tutto  Fermi National Accelerator
Laboratory (FNAL), Batavia, IL 60510, USA  S. R. Dennis  University of
Cambridge, Cambridge CB3 0HE, United Kingdom  P. Detje  University of
Cambridge, Cambridge CB3 0HE, United Kingdom  R. Diurba  Universität Bern,
Bern CH-3012, Switzerland  Z. Djurcic  Argonne National Laboratory (ANL),
Lemont, IL, 60439, USA  R. Dorrill  Illinois Institute of Technology (IIT),
Chicago, IL 60616, USA  K. Duffy  University of Oxford, Oxford OX1 3RH, United
Kingdom  S. Dytman  University of Pittsburgh, Pittsburgh, PA, 15260, USA  B.
Eberly  University of Southern Maine, Portland, ME, 04104, USA  P. Englezos
Rutgers University, Piscataway, NJ, 08854, USA  A. Ereditato  University of
Chicago, Chicago, IL, 60637, USA  Fermi National Accelerator Laboratory
(FNAL), Batavia, IL 60510, USA  J. J. Evans  The University of Manchester,
Manchester M13 9PL, United Kingdom  R. Fine  Los Alamos National Laboratory
(LANL), Los Alamos, NM, 87545, USA  W. Foreman  Illinois Institute of
Technology (IIT), Chicago, IL 60616, USA  B. T. Fleming  University of
Chicago, Chicago, IL, 60637, USA  D. Franco  University of Chicago, Chicago,
IL, 60637, USA  A. P. Furmanski  University of Minnesota, Minneapolis, MN,
55455, USA  F. Gao  University of California, Santa Barbara, CA, 93106, USA
D. Garcia-Gamez  Universidad de Granada, Granada E-18071, Spain  S. Gardiner
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  G. Ge
Columbia University, New York, NY, 10027, USA  S. Gollapinni  Los Alamos
National Laboratory (LANL), Los Alamos, NM, 87545, USA  E. Gramellini  The
University of Manchester, Manchester M13 9PL, United Kingdom  P. Green
University of Oxford, Oxford OX1 3RH, United Kingdom  H. Greenlee  Fermi
National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  L. Gu
Lancaster University, Lancaster LA1 4YW, United Kingdom  W. Gu  Brookhaven
National Laboratory (BNL), Upton, NY, 11973, USA  R. Guenette  The University
of Manchester, Manchester M13 9PL, United Kingdom  P. Guzowski  The University
of Manchester, Manchester M13 9PL, United Kingdom  L. Hagaman  University of
Chicago, Chicago, IL, 60637, USA  M. D. Handley  University of Cambridge,
Cambridge CB3 0HE, United Kingdom  O. Hen  Massachusetts Institute of
Technology (MIT), Cambridge, MA, 02139, USA  C. Hilgenberg  University of
Minnesota, Minneapolis, MN, 55455, USA  G. A. Horton-Smith  Kansas State
University (KSU), Manhattan, KS, 66506, USA  Z. Imani  Tufts University,
Medford, MA, 02155, USA  B. Irwin  University of Minnesota, Minneapolis, MN,
55455, USA  M. S. Ismail  University of Pittsburgh, Pittsburgh, PA, 15260, USA
C. James  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA
X. Ji  Nankai University, Nankai District, Tianjin 300071, China  J. H. Jo
Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  R. A. Johnson
University of Cincinnati, Cincinnati, OH, 45221, USA  Y.-J. Jwa  Columbia
University, New York, NY, 10027, USA  D. Kalra  Columbia University, New York,
NY, 10027, USA  N. Kamp  Massachusetts Institute of Technology (MIT),
Cambridge, MA, 02139, USA  G. Karagiorgi  Columbia University, New York, NY,
10027, USA  W. Ketchum  Fermi National Accelerator Laboratory (FNAL), Batavia,
IL 60510, USA  M. Kirby  Brookhaven National Laboratory (BNL), Upton, NY,
11973, USA  T. Kobilarcik  Fermi National Accelerator Laboratory (FNAL),
Batavia, IL 60510, USA  I. Kreslo  Universität Bern, Bern CH-3012, Switzerland
N. Lane  The University of Manchester, Manchester M13 9PL, United Kingdom
J.-Y. Li  University of Edinburgh, Edinburgh EH9 3FD, United Kingdom  Y. Li
Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  K. Lin  Rutgers
University, Piscataway, NJ, 08854, USA  B. R. Littlejohn  Illinois Institute
of Technology (IIT), Chicago, IL 60616, USA  H. Liu  Brookhaven National
Laboratory (BNL), Upton, NY, 11973, USA  W. C. Louis  Los Alamos National
Laboratory (LANL), Los Alamos, NM, 87545, USA  X. Luo  University of
California, Santa Barbara, CA, 93106, USA  C. Mariani  Center for Neutrino
Physics, Virginia Tech, Blacksburg, VA, 24061, USA  D. Marsden  The University
of Manchester, Manchester M13 9PL, United Kingdom  J. Marshall  University of
Warwick, Coventry CV4 7AL, United Kingdom  N. Martinez  Kansas State
University (KSU), Manhattan, KS, 66506, USA  D. A. Martinez Caicedo  South
Dakota School of Mines and Technology (SDSMT), Rapid City, SD, 57701, USA  S.
Martynenko  Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  A.
Mastbaum  Rutgers University, Piscataway, NJ, 08854, USA  I. Mawby  Lancaster
University, Lancaster LA1 4YW, United Kingdom  N. McConkey  Queen Mary
University of London, London E1 4NS, United Kingdom  V. Meddage  Kansas State
University (KSU), Manhattan, KS, 66506, USA  J. Mendez  Louisiana State
University, Baton Rouge, LA, 70803, USA  J. Micallef  Massachusetts Institute
of Technology (MIT), Cambridge, MA, 02139, USA  Tufts University, Medford, MA,
02155, USA  K. Miller  University of Chicago, Chicago, IL, 60637, USA  A.
Mogan  Colorado State University, Fort Collins, CO, 80523, USA  T. Mohayai
Indiana University, Bloomington, IN 47405, USA  M. Mooney  Colorado State
University, Fort Collins, CO, 80523, USA  A. F. Moor  University of Cambridge,
Cambridge CB3 0HE, United Kingdom  C. D. Moore  Fermi National Accelerator
Laboratory (FNAL), Batavia, IL 60510, USA  L. Mora Lepin  The University of
Manchester, Manchester M13 9PL, United Kingdom  M. M. Moudgalya  The
University of Manchester, Manchester M13 9PL, United Kingdom  S. Mulleriababu
Universität Bern, Bern CH-3012, Switzerland  D. Naples  University of
Pittsburgh, Pittsburgh, PA, 15260, USA  A. Navrer-Agasson  Imperial College
London, London SW7 2AZ, United Kingdom  The University of Manchester,
Manchester M13 9PL, United Kingdom  N. Nayak  Brookhaven National Laboratory
(BNL), Upton, NY, 11973, USA  M. Nebot-Guinot  University of Edinburgh,
Edinburgh EH9 3FD, United Kingdom  C. Nguyen  Rutgers University, Piscataway,
NJ, 08854, USA  J. Nowak  Lancaster University, Lancaster LA1 4YW, United
Kingdom  N. Oza  Columbia University, New York, NY, 10027, USA  O. Palamara
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  N.
Pallat  University of Minnesota, Minneapolis, MN, 55455, USA  V. Paolone
University of Pittsburgh, Pittsburgh, PA, 15260, USA  A. Papadopoulou  Argonne
National Laboratory (ANL), Lemont, IL, 60439, USA  V. Papavassiliou  New
Mexico State University (NMSU), Las Cruces, NM, 88003, USA  H. B. Parkinson
University of Edinburgh, Edinburgh EH9 3FD, United Kingdom  S. F. Pate  New
Mexico State University (NMSU), Las Cruces, NM, 88003, USA  N. Patel
Lancaster University, Lancaster LA1 4YW, United Kingdom  Z. Pavlovic  Fermi
National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  E. Piasetzky
Tel Aviv University, Tel Aviv, Israel, 69978  K. Pletcher  Michigan State
University, East Lansing, MI 48824, USA  I. Pophale  Lancaster University,
Lancaster LA1 4YW, United Kingdom  X. Qian  Brookhaven National Laboratory
(BNL), Upton, NY, 11973, USA  J. L. Raaf  Fermi National Accelerator
Laboratory (FNAL), Batavia, IL 60510, USA  V. Radeka  Brookhaven National
Laboratory (BNL), Upton, NY, 11973, USA  A. Rafique  Argonne National
Laboratory (ANL), Lemont, IL, 60439, USA  M. Reggiani-Guzzo  University of
Edinburgh, Edinburgh EH9 3FD, United Kingdom  L. Ren  New Mexico State
University (NMSU), Las Cruces, NM, 88003, USA  L. Rochester  SLAC National
Accelerator Laboratory, Menlo Park, CA, 94025, USA  J. Rodriguez Rondon  South
Dakota School of Mines and Technology (SDSMT), Rapid City, SD, 57701, USA  M.
Rosenberg  Tufts University, Medford, MA, 02155, USA  M. Ross-Lonergan  Los
Alamos National Laboratory (LANL), Los Alamos, NM, 87545, USA  I. Safa
Columbia University, New York, NY, 10027, USA  D. W. Schmitz  University of
Chicago, Chicago, IL, 60637, USA  A. Schukraft  Fermi National Accelerator
Laboratory (FNAL), Batavia, IL 60510, USA  W. Seligman  Columbia University,
New York, NY, 10027, USA  M. H. Shaevitz  Columbia University, New York, NY,
10027, USA  R. Sharankova  Fermi National Accelerator Laboratory (FNAL),
Batavia, IL 60510, USA  J. Shi  University of Cambridge, Cambridge CB3 0HE,
United Kingdom  E. L. Snider  Fermi National Accelerator Laboratory (FNAL),
Batavia, IL 60510, USA  M. Soderberg  Syracuse University, Syracuse, NY,
13244, USA  S. Söldner-Rembold  Imperial College London, London SW7 2AZ,
United Kingdom  The University of Manchester, Manchester M13 9PL, United
Kingdom  J. Spitz  University of Michigan, Ann Arbor, MI, 48109, USA  M.
Stancari  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA
J. St. John  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510,
USA  T. Strauss  Fermi National Accelerator Laboratory (FNAL), Batavia, IL
60510, USA  A. M. Szelc  University of Edinburgh, Edinburgh EH9 3FD, United
Kingdom  W. Tang  University of Tennessee, Knoxville, TN, 37996, USA  N.
Taniuchi  University of Cambridge, Cambridge CB3 0HE, United Kingdom  K. Terao
SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA  C. Thorpe
The University of Manchester, Manchester M13 9PL, United Kingdom  D. Torbunov
Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  D. Totani
University of California, Santa Barbara, CA, 93106, USA  M. Toups  Fermi
National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  A. Trettin
The University of Manchester, Manchester M13 9PL, United Kingdom  Y.-T. Tsai
SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA  J. Tyler
Kansas State University (KSU), Manhattan, KS, 66506, USA  M. A. Uchida
University of Cambridge, Cambridge CB3 0HE, United Kingdom  T. Usher  SLAC
National Accelerator Laboratory, Menlo Park, CA, 94025, USA  B. Viren
Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  J. Wang  Nankai
University, Nankai District, Tianjin 300071, China  M. Weber  Universität
Bern, Bern CH-3012, Switzerland  H. Wei  Louisiana State University, Baton
Rouge, LA, 70803, USA  A. J. White  University of Chicago, Chicago, IL, 60637,
USA  S. Wolbers  Fermi National Accelerator Laboratory (FNAL), Batavia, IL
60510, USA  T. Wongjirad  Tufts University, Medford, MA, 02155, USA  M.
Wospakrik  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510,
USA  K. Wresilo  University of Cambridge, Cambridge CB3 0HE, United Kingdom
W. Wu  University of Pittsburgh, Pittsburgh, PA, 15260, USA  E. Yandel
University of California, Santa Barbara, CA, 93106, USA  T. Yang  Fermi
National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  L. E. Yates
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  H. W. Yu
Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA  G. P. Zeller
Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA  J.
Zennamo  Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA
C. Zhang  Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA

###### Abstract

A significant challenge in measurements of neutrino oscillations is
reconstructing the incoming neutrino energies. While modern fully-active
tracking calorimeters such as liquid argon time projection chambers in
principle allow the measurement of all final state particles above some
detection threshold, undetected neutrons remain a considerable source of
missing energy with little to no data constraining their production rates and
kinematics. We present the first demonstration of tagging neutrino-induced
neutrons in liquid argon time projection chambers using secondary protons
emitted from neutron-argon interactions in the MicroBooNE detector. We
describe the method developed to identify neutrino-induced neutrons and
demonstrate its performance using neutrons produced in muon-neutrino charged
current interactions. The method is validated using a small subset of
MicroBooNE’s total dataset. The selection yields a sample with $60\%$ of
selected tracks corresponding to neutron-induced secondary protons.

##  I Introduction

With the study of neutrino oscillations moving into a precision measurement
era with the Deep Underground Neutrino Experiment (DUNE) and Hyper-Kamiokande
(Hyper-K) experiments under construction [1, 2], significant work is still
needed to reduce the uncertainties that arise due to the modeling of neutrino-
nucleus interactions. As neutrino energies are typically estimated based on
the visible final state particles produced in their interactions (e.g. [3, 4,
5]), the presence of neutrons will bias these estimates even for fully active
detectors such as the liquid argon time projection chamber (LArTPC) technology
that DUNE will use. While developments have been made in understanding and
improving neutrino energy resolutions using new methods [6, 7, 8], neutrons
will always represent a bias that requires model-dependent corrections. To
first order, the neutrino’s reconstructed energy will be biased by the full
kinetic energy of the neutron. While neutrons produced in neutrino
interactions can be observed through capture processes, particularly in
gadolinium-loaded detectors [10, 9], this technique is not easily applicable
to LArTPC detectors and provides little information other than the presence of
a neutron. Neutrino-induced neutrons can also be measured through inelastic
scatters with nuclei, for example using liquid scintillator detectors (as
demonstrated by the COHERENT experiment [11]) or in solid scintillator
detectors (e.g. in the MINERvA experiment [13, 12]). In the case of the
MINERvA experiment, these measurements were made using antineutrino
interactions which tend to produce more neutrons. It has also been shown using
the ArgoNeuT detector that neutrons can be identified in a LArTPC through the
photons produced by the de-excitations of excited argon nuclei produced in
neutron inelastic scatters [14]. This work introduces a method of identifying
neutrino-induced neutrons in a liquid argon TPC using neutron-argon inelastic
scatters that produce a secondary proton, similar to the technique
demonstrated by MINERvA. The method is validated using data from the
MicroBooNE detector.

###  I.1 MicroBooNE Detector

The MicroBooNE LArTPC detector is a $2.56$ ($x$) $\times$ $2.33$ ($y$)
$\times$ $10.37$ ($z$) m3, 85 tonne active volume LArTPC exposed to the
Booster Neutrino Beam (BNB) at Fermi National Accelerator Laboratory that took
data between 2015-2021, with an exposure of $1.49\times 10^{21}$ protons on
target (POT) [15]. This work uses a subset of that data, corresponding to an
exposure of $4.13\times 10^{19}$ POT delivered in 2017-2018. The MicroBooNE
detector is located $468$ m downstream of the target, at which point the
neutrino beam primarily consists of $\nu_{\mu}$ ($93.7\%$) with a mean energy
of $\mathtt{\sim}$800 MeV [16]. Charged particles produced in neutrino-argon
interactions propagate through the LArTPC and ionize the argon atoms. The
ionized charge drifts in a 273 V/cm electric field towards the anode that
consists of two induction planes and one collection plane, with 3 mm pitch
between wires and 3 mm plane separation [17]. The full drift time for an
electron in MicroBooNE is 2.3 ms. There is an average of six cosmogenic
particles in the detector during a drift window [18, 19]. The LArTPC is paired
with 32 photo-multiplier tubes (PMTs) that identify scintillation photons from
argon atoms excited by particles traversing the liquid argon. By leveraging
the time and spatial structure of the scintillation light, and comparing to
the visible TPC activity, cosmogenic backgrounds that are out of time with the
1.6 $\mu$s BNB spill can be efficiently tagged and removed.

###  I.2 MicroBooNE Simulation

The MicroBooNE collaboration uses software that employs a series of Monte
Carlo simulations that model all aspects of the experiment. The BNB flux
simulation was originally developed by the MiniBooNE collaboration [16] and
then customized for MicroBooNE. The neutrino-argon interactions are modeled in
the MicroBooNE simulation chain using the GENIE v3.0.6 neutrino event
generator [20, 21] with a modified version of the G18_10a_02_11a tune. This
modified tune of G18_10a_02_11a was developed to better fit the 2016 CC0$\pi$
T2K dataset [22]. Final state particles from the interactions simulated by
GENIE are propagated through the MicroBooNE detector using GEANTv4_10_3_p03c
[23, 24]. The physics package used in the MicroBooNE version of GEANT4 is the
quark-gluon string model and the Bertini cascade model (QGSP_BERT) [23]. Data
taken while the BNB is off is overlaid on the simulated neutrino interaction
to describe both the cosmic background and intrinsic detector noise. The
detector response is modeled in the LArSoft framework [25].

The neutrons of interest in this work are produced in neutrino interactions.
An additional source of neutrons comes from secondary interactions of
neutrino-induced hadrons within the detector volume. These neutrons are
distinct from the primary neutrons in that they originate from a different
nucleus. However, their signatures are indistinguishable and they constitute
an irreducible background.

Figure 1: Neutron-argon inelastic scattering cross section as a function of
neutron kinetic energy and broken down by the most common inelastic scatter
final states. The predicted neutron energy spectrum is shown in gray, with
arbitrary normalization.

##  II Neutron Detection

In order to tag and identify neutrons in neutrino interactions on argon, it is
important to understand their behavior as they propagate through the detector.
This section outlines the characteristics of neutron inelastic scattering on
argon.

Tables 1\- II summarize the behavior of neutrons in the MicroBooNE detector,
according to the GEANT4 simulation deployed. Importantly, of the neutrons in
our sample, $71.45\%$ inelastically scatter at least once within the detector
before leaving the active volume. The majority of neutrons inelastically
scatter only once ($30.28\%$) or twice ($26.51\%$). More than half ($54.13\%$)
of neutron inelastic scatters result in only the original neutron and the
possibly excited secondary argon atom, while another $30.86\%$ of scatters
produce additional neutrons. Finally, $8.85\%$ of neutron-argon inelastic
scatters create protons, with $6.60\%$ of scatters including one proton and
$1.37\%$ of scatters including two protons.

For BNB neutrino-induced neutrons in MicroBooNE, the flux-integrated cross
section is dominated by the 1 neutron and 2 or more neutron final states, due
to the low average neutron energies. Figure 1 shows the neutron-argon
inelastic scattering cross section (as extracted from the GEANT4 simulation)
as a function of the neutron kinetic energy and broken down by the final
states listed in Table II. The cross section is only shown for neutron
energies above 40 MeV, the rough minimum to which our analysis method –
reconstruction of a secondary proton track – is sensitive. The energy spectrum
predicted by the GENIE simulation for neutrons produced by neutrinos in
MicroBooNE is shown in gray, with arbitrary normalization. These theoretical
predictions have not been directly validated with data [26], although in the
energy range of interest the total (including elastic interactions) neutron-
argon cross section has been measured [27]. The neutron-argon total cross
section between neutron kinetic energies of 95 and 720 MeV is fairly flat and
approximately 700 mb, corresponding to a mean free path in liquid argon of
$\mathtt{\sim}$70 cm.

In the energy range of interest (between 40 and 600 MeV), the proton
production cross section is approximately flat. The detection method presented
here targets secondary protons produced by inelastic scatters, represented by
the pink and blue lines in Fig 1. Proton-generating processes become the
dominant predicted exclusive interaction channel above roughly 300 MeV in
neutron kinetic energy. A similar method has been shown to work to identify
neutrons in the ProtoDUNE detector [28].

Table 1: Final state neutron end processes. Neutron End Process |
---|---
Exit detector without inelastic scatter |  $24.63\%$
Exit detector after inelastic scatter |  $71.45\%$
Captured by argon |  $3.13\%$
Other |  $0.75\%$
Table 2: Number of times final state neutrons inelastically scatter. Number of Inelastic Scatters |
---|---
0 scatters |  $25.48\%$
1 scatter |  $30.28\%$
2 scatters |  $26.51\%$
3 scatters |  $11.87\%$
4+ scatters |  $5.87\%$
Table 3: Final state particles of neutron-argon inelastic scatters.

Final State of Scatter |
---|---
(1) $\ce{Ar}$* |  $54.13\%$
(2) $\ce{Ar}$* $+Nn$ |  $30.86\%$
(3) $\ce{Cl}+Nn+1p$ |  $6.60\%$
(4) $\ce{S}+Nn+2p$ |  $1.37\%$
(5) $\ce{S}+Nn+\alpha$ |  $2.14\%$
(6) Other proton final state |  $0.88\%$
(7) Other |  $4.03\%$

Figure 2: Data event showing $\nu_{\mu}$ CC candidate with a final state
neutron candidate. A secondary proton track candidate is seen in the middle of
the image. Image is of the collection plane. Here the horizontal axis shows
the z direction and the vertical axis shows the x direction in the MicroBooNE
detector. Charge deposited is represented by the color scale, where blue
corresponds to small charge deposits and red corresponds to large charge
deposits.  Figure 3: Neutrino vertex displacement for true secondary protons
created from final state neutrons. Error bars shown here only represent
statistical error.

(a)

(b)

Figure 4: (a) Kinetic energy spectrum for all $\nu_{\mu}$ CC final state true
neutrons. (b) Correlation between parent neutron kinetic energy and daughter
proton kinetic energy made from MicroBooNE simulation. Error bars shown here
only represent statistical error.

(a)

(b)

Figure 5: (a) Track length of true secondary protons created from final state
neutrons. (b) Area normalized number of protons produced by final state
neutrons. Error bars shown here only represent statistical error.

The most frequent type of neutron interaction leaves the argon nucleus in an
excited state without producing another visible signature. In principle,
MicroBooNE has the LArTPC detector response attributes and existing low-energy
reconstruction tools [29, 30] to tag neutrons through de-excitation photons.
While previous demonstrations of identifying such events have been produced
[14], these interactions present unique challenges that complicate their
application to the problem of neutrino calorimetry [31]. When leveraging low
energy photons, there is a contaminating population of photons from de-
excitation of the remnant nucleus from the initial neutrino interaction, as
well as a large amount of cosmogenic and radiogenic low energy EM activity,
especially in a surface LArTPC such as the MicroBooNE detector. In addition,
the isotropic nature of these de-excitation photons, along with a 14 cm
radiation length in LAr, means any directional information which could help
constrain the neutron’s kinematics is significantly smeared [14, 32]. The
detection method presented here targets instead the secondary protons produced
by inelastic scatters. While this interaction mode is less common, these
events present fewer problematic backgrounds, and the secondary proton
location can be used to better estimate the neutron direction. Additionally,
secondary protons will be most sensitive to higher energy neutrons which are
the most relevant when considering potential biases to neutrino energy
estimation.

It is worth noting that the number of neutrons that produce visible protons
depends on the detector geometry. With the MicroBooNE detector being less than
2.5 m in dimensions of width and height, all neutrino interactions are less
than 2 neutron interaction lengths from the nearest detector boundary. A
larger detector, such as those being constructed for DUNE, will therefore have
a higher efficiency for tagging neutrons.

Figure 2 shows a MicroBooNE data event display that includes a charged current
(CC) candidate interaction (upper left of image) with a final state neutron
candidate. This neutron re-scatters on an argon nucleus and produces the
proton track candidate (red track in the center of the image). Position
information from this track allows for the reconstruction of the direction of
the neutron, as well as the distance between the secondary proton and the
neutrino interaction vertex.

Identifying neutrons via secondary protons presents certain challenges. The
exponential distribution in Fig. 3 shows that many secondary protons appear
distant from the true neutrino vertex. Most neutrons produced in neutrino
interactions from the BNB are very low energy (Fig. 3(a)) and because neutrons
transfer a small amount of kinetic energy to the daughter protons, there is
minimal correlation between the neutron’s kinetic energy and the secondary
proton’s kinetic energy (Fig. 3(b)). This results in short proton tracks which
are hard to identify. Figure 4(a) shows the true secondary proton track length
distribution from simulation. The majority of the protons ($\mathtt{\sim}$74%)
created from final state neutrons travel less than 3 mm, meaning that they can
cross at most 1 wire from a single plane in the detector. Figure 4(b) shows
the number of protons produced by a final state neutron, showing that 70% of
neutrons do not produce any secondary protons at all. This apparent
disagreement with the results shown in Table II is because neutrons can
scatter multiple times, thus providing more opportunities to produce a proton.
Though this identification method leads to a very low efficiency for low
energy neutrons, it does identify higher-energy neutrons at a higher
efficiency.

##  III Event Selection

The reconstruction of charged particle tracks is performed by the Pandora
multi-algorithm pattern recognition toolkit [33] which we briefly describe
here. Hits on the three planes are passed to the Pandora cosmic reconstruction
to first identify unambiguous cosmic-ray muons and their daughter delta rays.
Hits associated with these backgrounds are removed from the input hit
collection. This hit collection is subsequently passed to the Pandora neutrino
reconstruction. Some cosmic tracks—the ones that are not obviously of cosmic
origin—are deliberately reconsidered in the next stage of the reconstruction
to avoid possible loss of neutrino-induced tracks at an early stage in the
process. The remaining hits are divided into groups, called slices, that
Pandora classifies as related using proximity and direction-based metrics with
the goal of isolating the various interactions in the detector, whether
induced by a neutrino or cosmic-ray. Cosmic-ray-oriented and neutrino-oriented
clustering and topological algorithms are separately applied to the hits in
each slice, so that the two outcomes can be compared. The most appropriate
outcome is identified for each slice. Collections of hits within each slice
are reconstructed to form particle flow particles (PFPs)—a general term that
can refer to either a shower or a track—and a topological score is calculated
for each slice using a support vector machine (SVM). Slices resembling a
neutrino interaction get a score closer to 1 and slices resembling cosmic
activity get a score closer to 0. Based on this score and confirming that the
candidate neutrino slice has a flash that is in time with the BNB spill, at
most one neutrino slice candidate per event is chosen. The slicing process is
especially relevant for the reconstruction of neutrons, since they regularly
scatter far away from the neutrino vertex and daughter protons are often not
included in the neutrino slice.

We begin our preselection with the sample of events identified as $\nu_{\mu}$
CC as in [34] where the neutrino vertex is contained in the fiducial volume:

  * 21.50 cm $\leq x\leq$ 234.85 cm,

  * -95.00 cm $\leq y\leq$ 95.00 cm,

  * 21.50 cm $\leq z\leq$ 966.80 cm.

We additionally require all candidate tracks to be fully contained in a
“containment volume” [34]:

  * 10.00 cm $\leq$ x $\leq$ 246.35 cm,

  * -106.50 cm $\leq$ y $\leq$ 106.50 cm,

  * 10.00 cm $\leq$ z $\leq$ 1026.80 cm.

We then consider all reconstructed PFPs within the neutrino slice to search
for secondary protons from primary neutrons. For the purpose of studying the
selection’s performance, we group these into a variety of categories based on
their origin:

Figure 6: Neutrino slice status for all fully contained tracks in $\nu_{\mu}$
CC events. Error bars shown on MicroBooNE data represent only statistical
uncertainty.

  1. 1.

Signal Proton: Track belongs to a secondary proton produced by a final state
neutron through neutron-inelastic scatter (final states (3), (4), and (6) in
Tab. II).

  2. 2.

Neutron Inelastic (NI) Proton (non-signal): Track belongs to a tertiary proton
that has been produced by a neutrino-generated, non-final state neutron
through neutron-inelastic scatter (final states (3), (4), and (6) in Tab. II).

  3. 3.

Primary Particle: Track belongs to a final state particle produced directly in
the neutrino interaction.

  4. 4.

Proton Inelastic (PI) Proton: Track belongs to a secondary proton produced
through proton-inelastic scatter.

  5. 5.

Other Particle: Track belongs to any other simulated particle.

  6. 6.

Dirt: Track originates from a neutrino interaction that occurred in the
detector hall or dirt surrounding the detector complex.

  7. 7.

Cosmics and MC EM: Track belongs to either simulated electromagnetic (EM)
activity or data cosmic activity overlaid on top of simulated neutrino event.

  8. 8.

BNB Beam-Off Data: Cosmic-induced backgrounds, measured with data taken when
the beam was off.

  9. 9.

BNB Beam-On Data.

(a)

(b)

(c)

(d)

Figure 7: All fully contained tracks in $\nu_{\mu}$ CC events that are
reconstructed in the neutrino slice. (a) track/shower SVM score, (b) LLR PID
score, (c) displacement between the candidate and the reconstructed neutrino
vertex, (d) candidate track length.

All figures showing MicroBooNE simulation have been normalized to the data
sample exposure used in this study ($4.13\times 10^{19}$ POT). BNB Beam-Off
Data has been normalized to the total number of triggers in the BNB Beam-On
Data sample. We define the integrated efficiency of the selection as the ratio
between the number of true neutrons tagged from daughter proton tracks to the
total number of true final state neutrons in the MC sample. The selection
purity is defined as the ratio of the number of signal proton tracks to the
number of selected candidate tracks. NI non-signal protons are a background of
particular interest. Neutrino-generated neutrons that are created from the
inelastic, secondary scatters of final state hadrons (primarily protons and
pions) with argon nuclei can then scatter again and create tertiary protons.
This background is effectively indistinguishable from our signal, however,
identifying these neutrons is useful. The presence of any neutrino-generated
neutrons indicates that there is missing energy, biasing the neutrino energy
reconstruction. Therefore, we will also report the NI produced proton purity,
defined here as the ratio of the number of signal and NI proton tracks to the
number of selected candidate tracks.

We require that neutron induced proton track candidates be reconstructed as
part of the neutrino interaction by Pandora. While this requirement rejects
the majority ($62.0\%$) of our signal, it helps reject $95.6\%$ of our
background. This information is shown in Fig. 6, where the large background
rejection achieved by discarding tracks not reconstructed as part of the
interaction (“Not in $\nu$ slice”) can be seen. Future iterations of the
analysis will attempt to recover these protons.

(a)

(b)

(c)

(d)

Figure 8: Fully contained, neutrino-slice tracks in $\nu_{\mu}$ CC events with
LLR PID Score $<$ $-0.15$, track/shower score $>0.1$, and that lie between
$10$ and $200$ cm of the reconstructed neutrino vertex. (a) Candidate
displacement from the reconstructed neutrino vertex, (b) candidate track
length, (c) Candidate proximity to nearest PFP, (d) and PFP relative direction
or cos($\chi$).

Once PFPs that are not included in the neutrino slice are eliminated, the
leading backgrounds are primary, BNB Beam-Off, Cosmic/EM MC tracks, and PI
protons (Fig. 7). The primary and PI proton backgrounds are both susceptible
to uncertainties on GEANT4 modeling of particle propagation and reinteraction
in the LAr. The MC EM background is sensitive to uncertainties from our
particle identification which relies on d$E$/d$x$ measurements. The dominant
uncertainty on this is from electron-ion recombination [35].

At this point, both the Cosmic and BNB Beam-Off backgrounds are predominantly
either muons or EM showers from muons. In order to eliminate them, we leverage
two reconstruction variables. The first variable is the track/shower score,
which is the output of an SVM in Pandora that determines if the PFP appears
more track-like or shower-like (Fig. 6(a)). Tracks have scores closer to 1,
while showers have scores closer to 0. The second reconstruction variable is
the log-likelihood ratio particle identification (LLR PID) score (Fig. 6(b))
and is calculated using MicroBooNE-specific software that runs after Pandora
completes its general reconstruction [36]. The score identifies tracks as more
muon-like (closer to 1) or more proton-like (closer to -1). Both scores have
been extensively vetted using MicroBooNE data and have been shown to be
minimally impacted by systematic uncertainties [34, 37].

We remove most primary tracks by measuring the displacement between the
candidate start point and the reconstructed neutrino vertex (Fig. 6(c)). After
correcting for electric field distortions in our calibrations [17], both this
variable and the proton candidate track length (Fig. 6(d)) are minimally
impacted by the space charge effect, with the largest effect when either the
neutrino vertex or neutron candidate are close to the edge of the TPC.

The cuts on track/shower score, LLR PID score, and reconstructed vertex
separation were optimized to maximize the product of the integrated neutron
detection efficiency and signal purity. The selection criteria are:

  * •

track/shower score $>0.1$ (Fig. 6(a)),

  * •

LLR PID score $<-0.15$ (Fig. 6(b)),

  * •

vertex separation $10<d<200$ cm (Fig. 6(c)).

The vertex separation and track length are shown in Fig. 7(a) and Fig. 7(b)
for the surviving tracks. At this point, the largest backgrounds are proton-
inelastic and primary protons. Unlike their neutron counterpart, the
initiating proton in proton-inelastic tracks is visible in the detector. The
secondary proton in proton inelastic interactions will inherently be in close
proximity to the parent proton, thereby differentiating it from our signal.
Assuming no nearby cosmic or primary PFPs, neutron produced proton tracks are
isolated in space. We measure the shortest distance between either end of the
secondary proton candidate, and the start and end points of every other PFP in
the detector. The minimum of the 4N calculations—with N+1 being the number of
PFPs in the detector including the current proton candidate—is the nearest PFP
proximity. The variable “candidate proximity to the nearest PFP” gives good
discriminating power between signal and these two proton backgrounds and is
shown in Fig. 7(c).

Assuming the neutron direction is given by a straight line from the
reconstructed neutrino vertex and the start of the proton candidate, we
measure the cosine of the relative angle ($\cos(\chi)$) between the neutron
direction and the candidate proton direction as seen in Fig. 7(d). The
candidate direction is defined as the vector between the reconstructed start
and end positions of the candidate track. At this point in the event
selection, the remaining cosmic, BNB Beam-Off, and Dirt tracks are likely
neutron-induced protons. Neutrino-generated tracks should be forward-peaked in
this variable due to momentum conservation whereas the other backgrounds
should not. As the relative direction is specifically designed to reduce the
neutron background that enters from outside the detector, it is particularly
susceptible to the uncertainty on modeling neutrino interactions in the dirt
around the detector [38]. However, the overall effect of dirt uncertainties
should be small as the dirt background is very small. The selection criteria
for both of the physics variables are:

  * •

candidate PFP proximity $>21$ cm,

  * •

candidate track $\cos\chi>0.2$.

Both the PFP proximity and relative direction will also be minimally affected
by the space charge effect in LArTPCs.

(a)

(b)

Figure 9: (a) Final candidate displacement from the reconstructed neutrino
vertex. (b) Final candidate track length.

(a)

(b)

(c)

(d)

Figure 10: (a) Neutron detection efficiency as a function of true neutron
kinetic energy. (b) Secondary proton detection efficiency. (c) Neutron
detection efficiency as a function of true neutron energy for neutrons that
produce protons with kinetic energy 50 MeV or more. (d) Neutron detection
efficiency as a function of neutron polar scattering angle for neutrons that
produce protons with kinetic energy 50 MeV or more.

##  IV Event Selection Performance

The reconstructed neutrino vertex displacement and track length for the final
set of neutron-generated, secondary proton track candidates is shown in Fig.
8(a) and 8(b). These distributions demonstrate that the modeling of neutron
inelastic scatters in MicroBooNE agrees with data within the sizeable
statistical uncertainties in this sample.

The final sample of neutron candidates is 48% pure primary neutrons, and 60%
pure neutrino-induced neutrons (including non-signal neutrons), with the
largest non-neutron backgrounds being cosmic particles, proton-inelastic
protons, and primary particles (also primarily protons). Due to our use of
data overlays, it is not possible to know exactly what type of cosmic
particles are being selected, but some fraction will be cosmogenic neutrons
scattering close to the neutrino interaction.

Figure 9(a) shows the neutron detection efficiency as a function of true
neutron kinetic energy at different stages in the event selection (error bars
in Fig. 10 represent only statistical uncertainty). The final efficiency is
negligible below neutron energies of around 100 MeV, rising to an
approximately flat efficiency of around 3% above 250 MeV. The secondary proton
detection efficiency as a function of true proton kinetic energy (Fig. 9(b))
shows that a significant source of inefficiency is failing to reconstruct and
select protons below 50 MeV kinetic energy. This efficiency measures the
detection rate of neutron-inelastically produced protons that start in the
TPC. There are a small number of neutrons that create multiple secondary
proton tracks that pass all event selection cuts. These neutrons are counted
once when determining the neutron efficiency, however Fig. 9(b) counts each
secondary proton independently. Due to this proton threshold, we report an
additional neutron detection efficiency as a function of neutron kinetic
energy for neutrons that create a proton in the TPC with more than 50 MeV of
true kinetic energy in Fig. 9(c). Lastly, we report the neutron detection
efficiency as a function of neutron polar scattering angle (defined as the
angle from the beam direction) which shows better detection efficiency for
neutrons that are more forward going (Fig. 9(d)). This higher detection
efficiency at low polar angle is due to several reasons: forward-going
neutrons tend to have higher energies leading to higher efficiency; the
detector geometry means forward-going neutrons have a lower chance of exiting
the detector before scattering; and the slicing performed by Pandora has some
dependence on direction, therefore forward-going tracks are more likely to be
classified as neutrino-like.

While the efficiency integrated across all neutron energies is low, the
majority of neutrons produced are very low energy. For neutrons that create
protons in the TPC with 50 MeV of kinetic energy or more, the integrated
efficiency is $8.4\%$.

The secondary proton energy threshold of approximately 50 MeV is consistent
with the threshold for primary protons in charged-current interactions [39].
However, due to the lack of hits from other particles in the same location,
these isolated charge depositions can be reconstructed at significantly lower
energies in principle. Further work to lower the proton threshold, including
leveraging newly developed low-threshold reconstruction capabilities [29, 30],
would lead to significant efficiency gains. Additionally, the selection could
be expanded to consider protons not reconstructed as part of the neutrino
slice – around 60% of neutron-induced protons in our sample.

##  V Summary and Conclusions

We have demonstrated the ability to identify neutrons produced in neutrino
interactions through the production of secondary protons from neutron
interactions separated from the neutrino vertex. The sample of neutron
candidates identified is 60% pure, with 48% purity when only considering
primary neutrons. While the integrated efficiency remains low, there remain
prospects for improvement. Additionally, the efficiency is lowest for low-
energy neutrons which contribute less to neutrino energy biases, and highest
for neutrons above 250 MeV kinetic energy, which could represent significant
biases to neutrino energy estimates.

The methods outlined in this paper would be applicable to any LArTPC and allow
for the measurement of neutrino-induced neutron production in MicroBooNE, the
SBN experiments, or DUNE in the future. Additionally, this method can be used
to provide statistical separation of neutrino and antineutrino events in a
non-magnetized LArTPC, and statistical separation of a sample of events known
to have biased neutrino energy estimators which may reduce uncertainties in an
oscillation measurement.

###### Acknowledgements.

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, HEP User Facility. Fermilab is managed by Fermi
Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359.
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 Interactions. 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.

## References

  * [1] B. Abi et al. (DUNE).“Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I Introduction to DUNE.” JINST 15 T08008, 2020. DOI: 10.1088/1748-0221/15/08/T08008
  * [2] K. Abe et al. (Hyper-Kamiokande). “Hyper-Kamiokande Design Report.” [arXiv:1805.04163 [physics.ins-det]].
  * [3] P. Adamson et al. (MINOS+ Collaboration). “Precision Constraints for Three-Flavor Neutrino Oscillations from the Full MINOS+ and MINOS Dataset.” Phys. Rev. Lett. 125 131802, 2020. DOI:10.1103/PhysRevLett.125.131802
  * [4] M. A. Acero et al. (NO$\nu$A Collaboration). “Improved measurement of neutrino oscillation parameters by the NO$\nu$A experiment.” Phys. Rev. D. 106 032004, 2022. DOI: 10.1103/PhysRevD.106.032004
  * [5] P. Abratenko et al. (MicroBooNE Collaboration). “First Constraints on Light Sterile Neutrino Oscillations from Combined Appearance and Disappearance Searches with the MicroBooNE Detector.” Phys. Rev. Lett. 130 011801, 2023. DOI: 10.1103/PhysRevLett.130.011801
  * [6] A. P. Furmanski and J. T. Sobczyk. “Neutrino energy reconstruction from one muon and one proton events.” Phys. Rev. C. 95 065501, 2017. DOI: 10.1103/PhysRevC.95.065501
  * [7] A. P. Furmanski and C. Hilgenberg. “Neutrino Energy Estimation in Neutral Current Interactions and Prospects for Sterile Neutrino Searches.” Phys. Rev. D. 103 112011, 2021. DOI: 10.1103/PhysRevD.103.112011
  * [8] J. Liu et al. “Deep-Learning-Based Kinematic Reconstruction for DUNE.” [arXiv:2012.06181 [physics.ins-det]].
  * [9] M. Ascencio-Sosa et al. (ANNIE Collaboration). “Deployment of Water-based Liquid Scintillator in the Accelerator Neutrino Neutron Interaction Experiment” [arXiv:2312.09335 [hep-ex]].
  * [10] M. Shinoki et al. (Super-Kamiokande Collaboration). “Measurement of the cosmogenic neutron yield in Super-Kamiokande with gadolinium loaded water” Phys. Rev. D. 107 092009, 2023. DOI: 10.1103/PhysRevD.107.092009
  * [11] P. An et al. “Measurement of ${}^{nat}\mathrm{Pb}({\nu}_{e},\mathrm{X}n)$ production with a stopped-pion neutrino source” Phys. Rev. D. 108 072001, 2023. DOI: 10.1103/PhysRevD.108.072001
  * [12] A. Olivier et al. ($\mathrm{MINER}\nu\mathrm{A}$ Collaboration).“Measurement of the multineutron ${\overline{\nu}}_{\mu}$ charged current differential cross section at low available energy on hydrocarbon” Phys. Rev. D. 108 112010, 2023. DOI: 10.1103/PhysRevD.108.112010
  * [13] M. Elkins et al. ($\mathrm{MINER}\nu\mathrm{A}$ Collaboration). “Neutron measurements from antineutrino hydrocarbon reactions” Phys. Rev. D. 100 052002, 2019. DOI: 10.1103/PhysRevD.100.052002
  * [14] R. Acciarri et al. (ArgoNeuT Collaboration). “Demonstration of MeV-scale physics in liquid argon time projection chambers using ArgoNeuT.” Phys. Rev. D 99, 012002, 2019. DOI: 10.1103/physrevd.99.012002.
  * [15] R. Acciarri et al. “Design and construction of the MicroBooNE detector” JINST 12 P02017, 2017 DOI: 10.1088/1748-0221/12/02/P02017
  * [16] A. A. Aguilar-Arevalo et al. (MiniBooNE Collaboration). “Neutrino Flux Prediction at MiniBooNE.” Phys. Rev. D 79, 072002, 2009. DOI: 10.1103/PhysRevD.79.072002.
  * [17] P. Abratenko et. al. (MicroBooNE Collaboration). “Measurement of space charge effects in the MicroBooNE LArTPC using cosmic muons.” JINST 15 P12037, 2020. DOI: 10.1088/1748-0221/15/12/p12037
  * [18] P. Abratenko et. al. (MicroBooNE Collaboration). “Measurement of the atmospheric muon rate with the MicroBooNE Liquid Argon TPC.” JINST 16 P04004, 2021. DOI: 10.1088/1748-0221/16/04/P04004
  * [19] D. Kaleko. “PMT triggering and readout for the MicroBooNE experiment.” JINST 8 C09009, 2013. DOI: 10.1088/1748-0221/8/09/C09009.
  * [20] C. Andreopoulos et al. . “The GENIE neutrino Monte Carlo generator.” Nucl. Instr. and Meth. A 614 1, 2010. DOI: 10.1016/j.nima.2009.12.009.
  * [21] L. Alvarez-Ruso et al. . “Recent highlights from GENIE v3.” Eur. Phys. J. Spec. Top. 230, 2021. DOI: 10.1140/epjs/s11734-021-00295-7.
  * [22] P. Abratenko et. al. (MicroBooNE collaboration). “New $\mathrm{CC}0\pi$ GENIE model tune for MicroBooNE.” Phys. Rev. D 105 072001, 2022. DOI: 10.1103/PhysRevD.105.072001
  * [23] S. Agostinelli et al.‘̇‘Geant4—a simulation toolkit.” Nucl. Instr. and Meth. A 506 3, 2003. DOI: 10.1016/S0168-9002(03)01368-8.
  * [24] J. Allison et al.‘̇‘Recent developments in Geant4.” Nucl. Instr. and Meth. A 835, 2016. DOI: 10.1016/j.nima.2016.06.125.
  * [25] E. L. Snider and G. Petrillo. “LArSoft: toolkit for simulation, reconstruction and analysis of liquid argon TPC neutrino detectors.” Journal of Physics: Conference Series 898, 2017. DOI: 10.1088/1742-6596/898/4/042057.
  * [26] S. Andringa et al. “Low-energy physics in neutrino LArTPCs” J. Phys. G: Nucl. Part. Phys. 50, 033001, 2023. DOI: 10.1088/1361-6471/acad17
  * [27] S. Martynenko et al. (CAPTAIN Collaboration). “Measurement of the neutron cross section on argon between 95 and 720 MeV.” Phys. Rev. D 107, 072009, 2023. DOI: 10.1103/PhysRevD.107.072009
  * [28] D. O. Rivera. “Neutron Cross Section Measurement In The ProtoDUNE-SP Experiment.” (FERMILAB-THESIS-2021-31) [Doctoral Dissertation, University of Pennsylvania].
  * [29] P. Abratenko et. al. (MicroBooNE collaboration). “Observation of radon mitigation in MicroBooNE by a liquid argon filtration system.” JINST 17 11, 2022. DOI: 10.1088/1748-0221/17/11/P11022.
  * [30] P. Abratenko et. al. (MicroBooNE collaboration). “Measurement of ambient radon progeny decay rates and energy spectra in liquid argon using the MicroBooNE detector” Phys. Rev. D 109, 052007, 2024 DOI:10.1103/PhysRevD.109.052007
  * [31] A. Friedland and S. Li. “Understanding the energy resolution of liquid argon neutrino detectors” Phys. Rev. D 99, 036009, 2019 DOI: 10.1103/PhysRevD.99.036009
  * [32] A. Friedland and S. Li. “Simulating hadron test beams in liquid argon” Phys. Rev. D 102, 096005, 2020 DOI: 10.1103/PhysRevD.102.096005
  * [33] R. Acciarri et al. (MicroBooNE Collaboration). “The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.” Eur. Phys. J. C 78 82, 2018. DOI: 10.1140/epjc/s10052-017-5481-6.
  * [34] S. Gardiner. “Double-differential Measurements of Mesonless Charged-Current Muon Neutrino Interactions on Argon with Final-State Protons using the MicroBooNE Detector.” DOI: 10.48550/arXiv.2403.19574
  * [35] C. Adams et. al. (MicroBooNE collaboration). “Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons.” JINST 15 P03022, 2020. DOI: 10.1088/1748-0221/15/03/P03022.
  * [36] P. Abratenko et. al. (MicroBooNE collaboration). “Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data.” J. High Energ. Phys. 2012 153, 2021. DOI: 10.1007/JHEP12(2021)153
  * [37] P. Abratenko et al. (MicroBooNE Collaboration). “Multidifferential cross section measurements of $\nu_{\mu}$-argon quasielasticlike reactions with the MicroBooNE detector.” Phys. Rev. D 108 053002, 2023. DOI: 10.1103/PhysRevD.108.053002.
  * [38] P. Abratenko et. al. (MicroBooNE Collaboration). “Search for an anomalous excess of inclusive charged-current ${\nu}_{e}$ interactions in the MicroBooNE experiment using Wire-Cell reconstruction.” Phys. Rev. D 105 112005, 2022. DOI: 10.1103/PhysRevD.105.112005
  * [39] P. Abratenko et. al. (MicroBooNE Collaboration). “Measurement of differential cross sections for ${\nu}_{\mu}$-Ar charged-current interactions with protons and no pions in the final state with the MicroBooNE detector.” Phys. Rev. D 102 112013, 2020. DOI: 10.1103/PhysRevD.102.112013