11institutetext: Abilene Christian University, Abilene, TX 79601, USA
22institutetext:  University of Albany, SUNY, Albany, NY 12222, USA
33institutetext:  University of Amsterdam, NL-1098 XG Amsterdam, The
Netherlands 44institutetext:  Antalya Bilim University, 07190
Döşemealtı/Antalya, Turkey 55institutetext:  University of Antananarivo,
Antananarivo 101, Madagascar 66institutetext:  Universidad Antonio Nariño,
Bogotá, Colombia 77institutetext:  Argonne National Laboratory, Argonne, IL
60439, USA 88institutetext:  University of Arizona, Tucson, AZ 85721, USA
99institutetext:  Universidad Nacional de Asunción, San Lorenzo, Paraguay
1010institutetext:  University of Athens, Zografou GR 157 84, Greece
1111institutetext:  Universidad del Atlántico, Barranquilla, Atlántico,
Colombia 1212institutetext:  Augustana University, Sioux Falls, SD 57197, USA
1313institutetext:  Banaras Hindu University, Varanasi - 221 005, India
1414institutetext:  University of Basel, CH-4056 Basel, Switzerland
1515institutetext:  University of Bern, CH-3012 Bern, Switzerland
1616institutetext:  Beykent University, Istanbul, Turkey 1717institutetext:
University of Birmingham, Birmingham B15 2TT, United Kingdom
1818institutetext:  Università del Bologna, 40127 Bologna, Italy
1919institutetext:  Boston University, Boston, MA 02215, USA
2020institutetext:  University of Bristol, Bristol BS8 1TL, United Kingdom
2121institutetext:  Brookhaven National Laboratory, Upton, NY 11973, USA
2222institutetext:  University of Bucharest, Bucharest, Romania
2323institutetext:  University of California Berkeley, Berkeley, CA 94720, USA
2424institutetext:  University of California Davis, Davis, CA 95616, USA
2525institutetext:  University of California Irvine, Irvine, CA 92697, USA
2626institutetext:  University of California Los Angeles, Los Angeles, CA
90095, USA 2727institutetext:  University of California Riverside, Riverside
CA 92521, USA 2828institutetext:  University of California Santa Barbara,
Santa Barbara, CA 93106, USA 2929institutetext:  California Institute of
Technology, Pasadena, CA 91125, USA 3030institutetext:  University of
Cambridge, Cambridge CB3 0HE, United Kingdom 3131institutetext:  Universidade
Estadual de Campinas, Campinas - SP, 13083-970, Brazil 3232institutetext:
Università di Catania, 2 - 95131 Catania, Italy 3333institutetext:
Universidad Católica del Norte, Antofagasta, Chile 3434institutetext:  Centro
Brasileiro de Pesquisas Físicas, Rio de Janeiro, RJ 22290-180, Brazil
3535institutetext:  IRFU, CEA, Université Paris-Saclay, F-91191 Gif-sur-
Yvette, France 3636institutetext:  CERN, The European Organization for Nuclear
Research, 1211 Meyrin, Switzerland 3737institutetext:  Institute of Particle
and Nuclear Physics of the Faculty of Mathematics and Physics of the Charles
University, 180 00 Prague 8, Czech Republic 3838institutetext:  University of
Chicago, Chicago, IL 60637, USA 3939institutetext:  Chung-Ang University,
Seoul 06974, South Korea 4040institutetext:  CIEMAT, Centro de Investigaciones
Energéticas, Medioambientales y Tecnológicas, E-28040 Madrid, Spain
4141institutetext:  University of Cincinnati, Cincinnati, OH 45221, USA
4242institutetext:  Centro de Investigación y de Estudios Avanzados del
Instituto Politécnico Nacional (Cinvestav), Mexico City, Mexico
4343institutetext:  Universidad de Colima, Colima, Mexico 4444institutetext:
University of Colorado Boulder, Boulder, CO 80309, USA 4545institutetext:
Colorado State University, Fort Collins, CO 80523, USA 4646institutetext:
Columbia University, New York, NY 10027, USA 4747institutetext:  Centro de
Tecnologia da Informacao Renato Archer, Amarais - Campinas, SP - CEP 13069-901
4848institutetext:  Central University of South Bihar, Gaya, 824236, India
4949institutetext:  Institute of Physics, Czech Academy of Sciences, 182 00
Prague 8, Czech Republic 5050institutetext:  Czech Technical University, 115
19 Prague 1, Czech Republic 5151institutetext:  Dakota State University,
Madison, SD 57042, USA 5252institutetext:  University of Dallas, Irving, TX
75062-4736, USA 5353institutetext:  Laboratoire d’Annecy de Physique des
Particules, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LAPP-IN2P3,
74000 Annecy, France 5454institutetext:  Daresbury Laboratory, Cheshire WA4
4AD, United Kingdom 5555institutetext:  Drexel University, Philadelphia, PA
19104, USA 5656institutetext:  Duke University, Durham, NC 27708, USA
5757institutetext:  Durham University, Durham DH1 3LE, United Kingdom
5858institutetext:  University of Edinburgh, Edinburgh EH8 9YL, United Kingdom
5959institutetext:  Universidad EIA, Envigado, Antioquia, Colombia
6060institutetext:  ETH Zurich, Zurich, Switzerland 6161institutetext:
Faculdade de Ciências da Universidade de Lisboa - FCUL, 1749-016 Lisboa,
Portugal 6262institutetext:  Universidade Federal de Alfenas, Poços de Caldas
- MG, 37715-400, Brazil 6363institutetext:  Universidade Federal de Goias,
Goiania, GO 74690-900, Brazil 6464institutetext:  Universidade Federal de São
Carlos, Araras - SP, 13604-900, Brazil 6565institutetext:  Universidade
Federal do ABC, Santo André - SP, 09210-580, Brazil 6666institutetext:
Universidade Federal do Rio de Janeiro, Rio de Janeiro - RJ, 21941-901, Brazil
6767institutetext:  Fermi National Accelerator Laboratory, Batavia, IL 60510,
USA 6868institutetext:  University of Ferrara, Ferrara, Italy
6969institutetext:  University of Florida, Gainesville, FL 32611-8440, USA
7070institutetext:  Fluminense Federal University, 9 Icaraí Niterói - RJ,
24220-900, Brazil 7171institutetext:  Università degli Studi di Genova,
Genova, Italy 7272institutetext:  Georgian Technical University, Tbilisi,
Georgia 7373institutetext:  University of Granada & CAFPE, 18002 Granada,
Spain 7474institutetext:  Gran Sasso Science Institute, L’Aquila, Italy
7575institutetext:  Laboratori Nazionali del Gran Sasso, L’Aquila AQ, Italy
7676institutetext:  University Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3,
38000 Grenoble, France 7777institutetext:  Universidad de Guanajuato,
Guanajuato, C.P. 37000, Mexico 7878institutetext:  Harish-Chandra Research
Institute, Jhunsi, Allahabad 211 019, India 7979institutetext:  Harvard
University, Cambridge, MA 02138, USA 8080institutetext:  University of Hawaii,
Honolulu, HI 96822, USA 8181institutetext:  University of Houston, Houston, TX
77204, USA 8282institutetext:  University of Hyderabad, Gachibowli, Hyderabad
- 500 046, India 8383institutetext:  Idaho State University, Pocatello, ID
83209, USA 8484institutetext:  Institut de Física d’Altes Energies
(IFAE)—Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
8585institutetext:  Instituto de Física Corpuscular, CSIC and Universitat de
València, 46980 Paterna, Valencia, Spain 8686institutetext:  Instituto Galego
de Fisica de Altas Enerxias, A Coruña, Spain 8787institutetext:  Illinois
Institute of Technology, Chicago, IL 60616, USA 8888institutetext:  Imperial
College of Science Technology and Medicine, London SW7 2BZ, United Kingdom
8989institutetext:  Indian Institute of Technology Guwahati, Guwahati, 781
039, India 9090institutetext:  Indian Institute of Technology Hyderabad,
Hyderabad, 502285, India 9191institutetext:  Indiana University, Bloomington,
IN 47405, USA 9292institutetext:  Istituto Nazionale di Fisica Nucleare
Sezione di Bologna, 40127 Bologna BO, Italy 9393institutetext:  Istituto
Nazionale di Fisica Nucleare Sezione di Catania, I-95123 Catania, Italy
9494institutetext:  Istituto Nazionale di Fisica Nucleare Sezione di Ferrara,
I-44122 Ferrara, Italy 9595institutetext:  Istituto Nazionale di Fisica
Nucleare Sezione di Genova, 16146 Genova GE, Italy 9696institutetext:
Istituto Nazionale di Fisica Nucleare Sezione di Lecce, 73100 - Lecce, Italy
9797institutetext:  Istituto Nazionale di Fisica Nucleare Sezione di Milano
Bicocca, 3 - I-20126 Milano, Italy 9898institutetext:  Istituto Nazionale di
Fisica Nucleare Sezione di Milano, 20133 Milano, Italy 9999institutetext:
Istituto Nazionale di Fisica Nucleare Sezione di Napoli, I-80126 Napoli, Italy
100100institutetext:  Istituto Nazionale di Fisica Nucleare Sezione di Padova,
35131 Padova, Italy 101101institutetext:  Istituto Nazionale di Fisica
Nucleare Sezione di Pavia, I-27100 Pavia, Italy 102102institutetext:  Istituto
Nazionale di Fisica Nucleare Laboratori Nazionali del Sud, 95123 Catania,
Italy 103103institutetext:  Universidad Nacional de Ingeniería, Lima 25, Perú
104104institutetext:  Institute for Nuclear Research of the Russian Academy of
Sciences, Moscow 117312, Russia 105105institutetext:  University of Insubria,
Via Ravasi, 2, 21100 Varese VA, Italy 106106institutetext:  University of
Iowa, Iowa City, IA 52242, USA 107107institutetext:  Iowa State University,
Ames, Iowa 50011, USA 108108institutetext:  Institut de Physique des 2 Infinis
de Lyon, 69622 Villeurbanne, France 109109institutetext:  Institute for
Research in Fundamental Sciences, Tehran, Iran 110110institutetext:  Instituto
Superior Técnico - IST, Universidade de Lisboa, 1049-001 Lisboa, Portugal
111111institutetext:  Iwate University, Morioka, Iwate 020-8551, Japan
112112institutetext:  University of Jammu, Jammu-180006, India
113113institutetext:  Jawaharlal Nehru University, New Delhi 110067, India
114114institutetext:  Jeonbuk National University, Jeonrabuk-do 54896, South
Korea 115115institutetext:  Joint Institute for Nuclear Research, Dzhelepov
Laboratory of Nuclear Problems 6 Joliot-Curie, Dubna, Moscow Region, 141980 RU
116116institutetext:  University of Jyvaskyla, FI-40014, Finland
117117institutetext:  Kansas State University, Manhattan, KS 66506, USA
118118institutetext:  Kavli Institute for the Physics and Mathematics of the
Universe, Kashiwa, Chiba 277-8583, Japan 119119institutetext:  High Energy
Accelerator Research Organization (KEK), Ibaraki, 305-0801, Japan
120120institutetext:  Korea Institute of Science and Technology Information,
Daejeon, 34141, South Korea 121121institutetext:  K L University, Vaddeswaram,
Andhra Pradesh 522502, India 122122institutetext:  National Institute of
Technology, Kure College, Hiroshima, 737-8506, Japan 123123institutetext:
Taras Shevchenko National University of Kyiv, 01601 Kyiv, Ukraine
124124institutetext:  Lancaster University, Lancaster LA1 4YB, United Kingdom
125125institutetext:  Lawrence Berkeley National Laboratory, Berkeley, CA
94720, USA 126126institutetext:  Laboratório de Instrumentação e Física
Experimental de Partículas, 1649-003 Lisboa and 3004-516 Coimbra, Portugal
127127institutetext:  University of Liverpool, L69 7ZE, Liverpool, United
Kingdom 128128institutetext:  Los Alamos National Laboratory, Los Alamos, NM
87545, USA 129129institutetext:  Louisiana State University, Baton Rouge, LA
70803, USA 130130institutetext:  University of Lucknow, Uttar Pradesh 226007,
India 131131institutetext:  Madrid Autonoma University and IFT UAM/CSIC, 28049
Madrid, Spain 132132institutetext:  Johannes Gutenberg-Universität Mainz,
55122 Mainz, Germany 133133institutetext:  University of Manchester,
Manchester M13 9PL, United Kingdom 134134institutetext:  Massachusetts
Institute of Technology, Cambridge, MA 02139, USA 135135institutetext:  Max-
Planck-Institut, Munich, 80805, Germany 136136institutetext:  University of
Medellín, Medellín, 050026 Colombia 137137institutetext:  University of
Michigan, Ann Arbor, MI 48109, USA 138138institutetext:  Michigan State
University, East Lansing, MI 48824, USA 139139institutetext:  Università del
Milano-Bicocca, 20126 Milano, Italy 140140institutetext:  Università degli
Studi di Milano, I-20133 Milano, Italy 141141institutetext:  University of
Minnesota Duluth, Duluth, MN 55812, USA 142142institutetext:  University of
Minnesota Twin Cities, Minneapolis, MN 55455, USA 143143institutetext:
University of Mississippi, University, MS 38677 USA 144144institutetext:
University of New Mexico, Albuquerque, NM 87131, USA 145145institutetext:  H.
Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences,
Cracow, Poland 146146institutetext:  Nikhef National Institute of Subatomic
Physics, 1098 XG Amsterdam, Netherlands 147147institutetext:  University of
North Dakota, Grand Forks, ND 58202-8357, USA 148148institutetext:  Northern
Illinois University, DeKalb, IL 60115, USA 149149institutetext:  Northwestern
University, Evanston, Il 60208, USA 150150institutetext:  University of Notre
Dame, Notre Dame, IN 46556, USA 151151institutetext:  Occidental College, Los
Angeles, CA 90041 152152institutetext:  Ohio State University, Columbus, OH
43210, USA 153153institutetext:  Oregon State University, Corvallis, OR 97331,
USA 154154institutetext:  National Centre for Nuclear Research, A. Soltana 7,
05 400 Otwock, Poland 155155institutetext:  University of Oxford, Oxford, OX1
3RH, United Kingdom 156156institutetext:  Pacific Northwest National
Laboratory, Richland, WA 99352, USA 157157institutetext:  Universtà degli
Studi di Padova, I-35131 Padova, Italy 158158institutetext:  Panjab
University, Chandigarh, 160014 U.T., India 159159institutetext:  Université
Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France 160160institutetext:
Université de Paris, CNRS, Astroparticule et Cosmologie, F-75006, Paris,
France 161161institutetext:  Università degli Studi di Parma, I-43121 Parma,
Italy 162162institutetext:  Università degli Studi di Pavia, 27100 Pavia PV,
Italy 163163institutetext:  University of Pennsylvania, Philadelphia, PA
19104, USA 164164institutetext:  Pennsylvania State University, University
Park, PA 16802, USA 165165institutetext:  Physical Research Laboratory,
Ahmedabad 380 009, India 166166institutetext:  Università di Pisa, I-56127
Pisa, Italy 167167institutetext:  University of Pittsburgh, Pittsburgh, PA
15260, USA 168168institutetext:  Pontificia Universidad Católica del Perú,
Lima, Perú 169169institutetext:  University of Puerto Rico, Mayaguez 00681,
Puerto Rico, USA 170170institutetext:  Punjab Agricultural University,
Ludhiana 141004, India 171171institutetext:  Queen Mary University of London,
London E1 4NS, United Kingdom 172172institutetext:  Radboud University,
NL-6525 AJ Nijmegen, Netherlands 173173institutetext:  University of
Rochester, Rochester, NY 14627, USA 174174institutetext:  Royal Holloway
College London, TW20 0EX, United Kingdom 175175institutetext:  Rutgers
University, Piscataway, NJ, 08854, USA 176176institutetext:  STFC Rutherford
Appleton Laboratory, Didcot OX11 0QX, United Kingdom 177177institutetext:
Università del Salento, 73100 Lecce, Italy 178178institutetext:  San Jose
State University, San José, CA 95192-0106, USA 179179institutetext:
Universidad Sergio Arboleda, 11022 Bogotá, Colombia 180180institutetext:
University of Sheffield, Sheffield S3 7RH, United Kingdom 181181institutetext:
SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
182182institutetext:  University of South Carolina, Columbia, SC 29208, USA
183183institutetext:  South Dakota School of Mines and Technology, Rapid City,
SD 57701, USA 184184institutetext:  South Dakota State University, Brookings,
SD 57007, USA 185185institutetext:  Southern Methodist University, Dallas, TX
75275, USA 186186institutetext:  Stony Brook University, SUNY, Stony Brook, NY
11794, USA 187187institutetext:  Sun Yat-Sen University, Guangzhou, 510275
188188institutetext:  Sanford Underground Research Facility, Lead, SD, 57754,
USA 189189institutetext:  University of Sussex, Brighton, BN1 9RH, United
Kingdom 190190institutetext:  Syracuse University, Syracuse, NY 13244, USA
191191institutetext:  Universidade Tecnológica Federal do Paraná, Curitiba,
Brazil 192192institutetext:  Texas A&M University, College Station, Texas
77840 193193institutetext:  Texas A&M University - Corpus Christi, Corpus
Christi, TX 78412, USA 194194institutetext:  University of Texas at Arlington,
Arlington, TX 76019, USA 195195institutetext:  University of Texas at Austin,
Austin, TX 78712, USA 196196institutetext:  University of Toronto, Toronto,
Ontario M5S 1A1, Canada 197197institutetext:  Tufts University, Medford, MA
02155, USA 198198institutetext:  Universidade Federal de São Paulo, 09913-030,
São Paulo, Brazil 199199institutetext:  Ulsan National Institute of Science
and Technology, Ulsan 689-798, South Korea 200200institutetext:  University
College London, London, WC1E 6BT, United Kingdom 201201institutetext:  Valley
City State University, Valley City, ND 58072, USA 202202institutetext:
Variable Energy Cyclotron Centre, 700 064 West Bengal, India
203203institutetext:  Virginia Tech, Blacksburg, VA 24060, USA
204204institutetext:  University of Warsaw, 02-093 Warsaw, Poland
205205institutetext:  University of Warwick, Coventry CV4 7AL, United Kingdom
206206institutetext:  Wellesley College, Wellesley, MA 02481, USA
207207institutetext:  Wichita State University, Wichita, KS 67260, USA
208208institutetext:  College of William and Mary, Williamsburg, VA 23187, USA
209209institutetext:  University of Wisconsin Madison, Madison, WI 53706, USA
210210institutetext:  Yale University, New Haven, CT 06520, USA
211211institutetext:  Yerevan Institute for Theoretical Physics and Modeling,
Yerevan 0036, Armenia 212212institutetext:  York University, Toronto M3J 1P3,
Canada

# Separation of track– and shower–like energy deposits in ProtoDUNE-SP using a
convolutional neural network

The DUNE Collaboration

A. Abed AbudLiverpool,CERN  B. AbiOxford  R. AcciarriFermi  M. A.
AceroAtlantico  M. R. AdamesTecnologica  G. AdamovGeorgian  M. AdamowskiFermi
D. AdamsBrookhaven  M. AdinolfiBristol  A. AduszkiewiczHouston  J.
AguilarLawrenceBerkeley  Z. AhmadVariableEnergy  J. AhmedWarwick  B.
AimardDannecyleVieux  B. Ali-MohammadzadehINFNCatania,CataniaUniversitadi  T.
AlionSussex  K. AllisonColoradoBoulder  S. Alonso MonsalveCERN,ETH  M.
AlRashedKansasstate  C. AltETH  A. AltonAugustana  R. AlvarezCIEMAT  P.
AmedoIGFAE  J. AndersonArgonne  C. AndreopoulosRutherford,Liverpool  M.
AndreottiINFNFerrara,Ferrarauniv  M. AndrewsFermi  F. AndrianalaAntananarivo
S. AndringaLIP  N. AnfimovJINR  A. AnkowskiSLAC  M. AntoniassiTecnologica  M.
AntonovaIFIC  A. AntoshkinJINR  S. AntuschBasel  A. Aranda-FernandezColima  L.
ArellanoManchester  L. O. ArnoldColumbia  M. A. ArroyaveEIA  J.
AsaadiTexasArlington  L. AsquithSussex  A. AurisanoCincinnati  V. AushevKyiv
D. AutieroIPLyon  V. Ayala LaraIngenieria  M. Ayala-TorresCinvestav  F.
AzfarOxford  M. BabiczCERN  A. BackIndiana  H. BackPacificNorthwest  J. J.
BackWarwick  C. BackhouseUniversityCollegeLondon  I. BagaturiaGeorgian  L.
BagbyFermi  N. BalashovJINR  S. BalasubramanianFermi  P. BaldiCalIrvine  B.
BallerFermi  B. BambahHyderabad  F. BaraoLIP,ISTlisboa  G. BarenboimIFIC  G.
BarkerWarwick  W. BarkhouseNorthdakota  C. BarnesMichigan  G. BarrOxford  J.
Barranco MonarcaGuanajuato  A. BarrosTecnologica  N. BarrosLIP,FCULport  J. L.
BarrowMassinsttech  A. Basharina-FreshvilleUniversityCollegeLondon  A.
BashyalArgonne  V. BasqueManchester  C. BatchelorEdinburgh  E. Batista das
ChagasCampinas  J. BattatWellesley  F. BattistiOxford  F. BayAntalya  M. C. Q.
BazettoCampinas  J. Bazo AlbaPontificia  J. F. BeacomOhiostate  E.
BechetoilleIPLyon  B. BeheraColoradoState  C. BeigbederParissaclay  L.
BellantoniFermi  G. BellettiniPisa  V. BelliniINFNCatania,CataniaUniversitadi
O. BeltramelloCERN  N. BenekosCERN  C. Benitez MontielAsuncion  F. Bento
NevesLIP  J. BergerColoradoState  S. BerkmanFermi  P.
BernardiniINFNLecce,Salento  R. M. BernerBern  A. BersaniINFNGenova  S.
BertolucciINFNBologna,BolognaUniversity  M. BetancourtFermi  A. Betancur
RodríguezEIA  A. BevanQMUL  Y. BezawadaCalDavis  T. S. BezerraSussex  A.
BhardwajLouisanastate  V. BhatnagarPanjab  M. BhattacharjeeIndGuwahati  D.
BhattaraiMississippi  S. BhullerBristol  B. BhuyanIndGuwahati  S. BiagiINFNSud
J. BianCalIrvine  M. BiassoniINFNMilanBicocca  K. BieryFermi  B.
BilkiBeykent,Iowa  M. BishaiBrookhaven  A. BitadzeManchester  A.
BlakeLancaster  F. BlaszczykFermi  G. BlazeyNorthernillinois  E.
BlucherChicago  J. BoissevainLosAlmos  S. BolognesiCEASaclay  T.
BoltonKansasstate  L. BombenINFNMilanBicocca,Insubria  M.
BonesiniINFNMilanBicocca,MilanoBicocca  M. BongrandParissaclay  C. Bonilla-
DiazCatolica  F. BoniniBrookhaven  A. BoothQMUL  F. BoranBeykent  S.
BordoniCERN  A. BorkumSussex  N. BostanNotreDame  P. BourCzechTechnical  C.
BourgeoisParissaclay  D. BoydenNorthernillinois  J. BracinikBirmingham  D.
BragaFermi  D. BrailsfordLancaster  A. BrancaINFNMilanBicocca  A.
BrandtTexasArlington  J. BremerCERN  D. BretonParissaclay  C. BrewRutherford
S. J. BriceFermi  C. BrizzolariINFNMilanBicocca,MilanoBicocca  C.
BrombergMichiganstate  J. BrookeBristol  A. BrossFermi  G.
BrunettiINFNMilanBicocca,MilanoBicocca  M. BrunettiWarwick  N.
BuchananColoradoState  H. BuddRochester  I. ButorovJINR  I.
CagnoliINFNBologna,BolognaUniversity  T. CaiYork  D. CaiuloIPLyon  R.
CalabreseINFNFerrara,Ferrarauniv  P. CalafiuraLawrenceBerkeley  J.
CalcuttOregonState  M. CalinBucharest  S. CalvezColoradoState  E. CalvoCIEMAT
A. CaminataINFNGenova  M. CampanelliUniversityCollegeLondon  D. CaratelliFermi
D. CarberColoradoState  J. CarcellerUniversityCollegeLondon  G.
CariniBrookhaven  B. CarlusIPLyon  M. F. CarneiroBrookhaven  P.
CarnitiINFNMilanBicocca  I. Caro TerrazasColoradoState  H.
CarranzaTexasArlington  T. CarrollWisconsin  J. F. Castaño ForeroAntonioNarino
A. CastilloSergioArboleda  C. CastromonteIngenieria  E. Catano-MurWilliamMary
C. CattadoriINFNMilanBicocca  F. CavalierParissaclay  G.
CavallaroINFNMilanBicocca  F. CavannaFermi  S. CentroPadova,INFNPadova  G.
CeratiFermi  A. CervelliINFNBologna  A. Cervera VillanuevaIFIC  M.
ChalifourCERN  A. ChappellWarwick  E. ChardonnetParisuniversite  N.
CharitonidisCERN  A. ChatterjeePitt  S. ChattopadhyayVariableEnergy  M.
Chavarry NeyraIngenieria  H. ChenBrookhaven  M. ChenCalIrvine  Y. ChenBern  Z.
ChenStonyBrook  Z. Chen-WishartRoyalholloway  Y. CheonUNIST  D.
CherdackHouston  C. ChiColumbia  S. ChildressFermi  R. ChircoIllinoisinstitute
A. ChiriacescuBucharest  G. ChisnallSussex  K. ChoKISTI  S.
ChoateNorthernillinois  D. ChokheliGeorgian  P. S. ChongPenn  A.
ChristensenColoradoState  D. ChristianFermi  G. ChristodoulouCERN  A.
ChukanovJINR  M. ChungUNIST  E. ChurchPacificNorthwest  V.
CiceroINFNBologna,BolognaUniversity  P. ClarkeEdinburgh  G.
ClineLawrenceBerkeley  T. E. CoanSouthernMethodist  A. G. CoccoINFNNapoli  J.
CoelhoParisuniversite  N. ColtonColoradoState  E. ConleyDuke  R. ConleySLAC
J. ConradMassinsttech  M. ConverySLAC  S. CopelloINFNGenova  P.
CovaINFNMilano,ParmaUniv  L. CremaldiMississippi  L. CremonesiQMUL  J. I.
Crespo-AnadónCIEMAT  M. CrislerFermi  E. CristaldoAsuncion  J.
CrnkovicMississippi  R. CrossLancaster  A. CuddColoradoBoulder  C.
CuestaCIEMAT  Y. CuiCalRiverside  D. CussansBristol  O. DalagerCalIrvine  H.
Da MottaCBPF  L. Da Silva PeresFederaldoRio  C. DavidYork,Fermi  Q.
DavidIPLyon  G. S. DaviesMississippi  S. DaviniINFNGenova  J.
DawsonParisuniversite  K. DeTexasArlington  S. DeAlbanysuny  P. DebbinsIowa
I. De BonisDannecyleVieux  M. DecowskiNikhef,Amsterdam  A. De
GouveaNorthwestern  P. C. De HolandaCampinas  I. L. De Icaza AstizSussex  A.
DeistingRoyalholloway  P. De JongNikhef,Amsterdam  A. DelbartCEASaclay  D.
DelepineGuanajuato  M. DelgadoINFNMilanBicocca,MilanoBicocca  A.
Dell’AcquaCERN  N. DelmonteINFNMilano,ParmaUniv  P. De LurgioArgonne  J. R. De
Mello NetoFederaldoRio  D. M. DeMuthValleyCity  S. DennisCambridge  C.
DenshamRutherford  G. W. DeptuchBrookhaven  A. De RoeckCERN  V. De RomeriIFIC
G. De SouzaCampinas  R. DeviJammu  R. DharmapalanHawaii  M. DiasUnifesp  F.
DiazPontificia  J. DiazIndiana  S. Di DomizioINFNGenova,Genova  L. Di
GiulioCERN  P. DingFermi  L. Di NotoINFNGenova,Genova  G. DirkxImperial  C.
DistefanoINFNSud  R. DiurbaMinntwin  M. DiwanBrookhaven  Z. DjurcicArgonne  D.
DoeringSLAC  S. DolanCERN  F. DolekBeykent  M. DolinskiDrexel  L. DomineSLAC
Y. DononCERN  D. DouglasMichiganstate  D. DouilletParissaclay  A. DragoneSLAC
G. DrakeFermi  F. DrielsmaSLAC  L. DuarteUnifesp  D. DuchesneauDannecyleVieux
K. DuffyFermi  P. DunneImperial  B. DuttaTexasAMcollege  H.
DuyangSouthcarolina  O. DvornikovHawaii  D. DwyerLawrenceBerkeley  A.
DyshkantNorthernillinois  M. EadsNorthernillinois  A. EarleSussex  D.
EdmundsMichiganstate  J. EischFermi  L. EmbergerManchester,Maxplanck  S.
EmeryCEASaclay  P. EnglezosRutgers  A. EreditatoYale  T. ErjavecCalDavis  C.
EscobarFermi  G. EurinCEASaclay  J. J. EvansManchester  E. EwartIndiana  A. C.
EzeribeSheffield  K. FaheyFermi  A. FalconeINFNMilanBicocca,MilanoBicocca  M.
Fani’LosAlmos  C. FarneseINFNPadova  Y. FarzanIPM  D. FedoseevJINR  J.
FelixGuanajuato  Y. FengIowaState  E. Fernandez-MartinezMadrid  P. Fernandez
MenendezIFIC  M. Fernandez MoralesIGFAE  F. FerraroINFNGenova,Genova  L.
FieldsNotreDame  P. FilipCzechAcademyofSciences  F. FilthautNikhef,Radboud  M.
FioriniINFNFerrara,Ferrarauniv  V. FischerIowaState  R. S. FitzpatrickMichigan
W. FlanaganDallas  B. FlemingYale  R. FlightRochester  S. FogartyColoradoState
W. ForemanIllinoisinstitute  J. FowlerDuke  W. FoxIndiana  J.
FrancCzechTechnical  K. FrancisNorthernillinois  D. FrancoYale  J.
FreemanFermi  J. FreestoneManchester  J. FriedBrookhaven  A. FriedlandSLAC  F.
Fuentes RobayoBristol  S. FuessFermi  I. K. FuricFlorida  K. FurmanQMUL  A. P.
FurmanskiMinntwin  A. GabrielliINFNBologna  A. GagoPontificia  H.
GallagherTufts  A. GallasParissaclay  A. Gallego-RosCIEMAT  N.
GalliceINFNMilano,MilanoUniv  V. GalymovIPLyon  E. GamberiniCERN  T.
GambleSheffield  F. GanacimTecnologica  R. GandhiHarish  R.
GandrajulaMichiganstate  F. GaoPitt  S. GaoBrookhaven  D. Garcia-GamezGranada
M. Á. García-PerisIFIC  S. GardinerFermi  D. GastlerBoston  J.
GauvreauOccidental  G. GeColumbia  N. GeffroyDannecyleVieux  B. GelliCampinas
A. GendottiETH  S. GentSouthDakotaState  Z. Ghorbani-MoghaddamINFNGenova  P.
GiammariaCampinas  T. GiammariaINFNFerrara,Ferrarauniv  N. GiangiacomiToronto
D. GibinPadova,INFNPadova  I. Gil-BotellaCIEMAT  S. GilliganOregonState  C.
GirerdIPLyon  A. GiriIndHyderabad  D. GnaniLawrenceBerkeley  O. GogotaKyiv  M.
GoldNewmexico  S. GollapinniLosAlmos  K. GollwitzerFermi  R. A.
GomesFederaldeGoias  L. Gomez BermeoSergioArboleda  L. S. Gomez
FajardoSergioArboleda  F. GonnellaBirmingham  D. Gonzalez-DiazIGFAE  M.
Gonzalez-LopezMadrid  M. C. GoodmanArgonne  O. GoodwinManchester  S.
GoswamiPhysicalResearchLaboratory  C. GottiINFNMilanBicocca  E.
GoudzovskiBirmingham  C. GraceLawrenceBerkeley  R. GranMinnduluth  E.
GranadosGuanajuato  P. GrangerCEASaclay  A. GrantDaresbury  C. GrantBoston  D.
GratieriFluminense  P. GreenManchester  L. GreenlerWisconsin  J. GreerBristol
J. GrenardCERN  C. GriffithSussex  M. GrohColoradoState  J. GrudzinskiArgonne
K. GrzelakWarsaw  W. GuBrookhaven  E. GuardincerriLosAlmos  V. GuarinoArgonne
M. GuariseINFNFerrara,Ferrarauniv  R. GuenetteHarvard  E. GuerardParissaclay
M. GuerzoniINFNBologna  D. GuffantiINFNMilano  A. GuglielmiINFNPadova  B.
GuoSouthcarolina  A. GuptaSLAC  V. GuptaNikhef  K. GuthikondaKL  R.
GutierrezAntonioNarino  P. GuzowskiManchester  M. M. GuzzoCampinas  S.
GwonChungAng  C. HaChungAng  K. HaafFermi  A. HabigMinnduluth  H.
HadavandTexasArlington  R. HaenniBern  A. HahnFermi  J.
HaistonSouthDakotaSchool  P. Hamacher-BaumannOxford  T. HamernikFermi  P.
HamiltonImperial  J. HanPitt  D. A. HarrisYork,Fermi  J. HartnellSussex  T.
HartnettRutherford  J. HartonColoradoState  T. HasegawaKEK  C. HasnipOxford
R. HatcherFermi  K. W. HatfieldCalIrvine  A. HatzikoutelisSanjosestate  C.
HayesIndiana  K. HayrapetyanQMUL  J. HaysQMUL  E. HazenBoston  M. HeHouston
A. HeaveyFermi  K. M. HeegerYale  J. HeiseSURF  S. HenryRochester  M.
Hernandez MorquechoIllinoisinstitute  K. HernerFermi  V HewesCincinnati  C.
HilgenbergMinntwin  T. HillIdaho  S. J. HillierBirmingham  A. HimmelFermi  E.
HinkleChicago  L. R. HirschTecnologica  J. HoHarvard  J. HoffFermi  A.
HolinRutherford  E. HoppePacificNorthwest  G. A. Horton-SmithKansasstate  M.
HostertMinntwin  A. HourlierMassinsttech  B. HowardFermi  R. HowellRochester
J. HoyosMedellin  I. HristovaRutherford  M. S. HronekFermi  J. HuangCalDavis
Z. HulcherSLAC  G. IlesImperial  N. IlicToronto  A. M. IliescuINFNBologna  R.
IllingworthFermi  G. IngrattaINFNBologna,BolognaUniversity  A.
IoannisianYerevan  B. IrwinMinntwin  L. IsenhowerAbilene  R. ItaySLAC  C. M.
JacksonPacificNorthwest  V. JainAlbanysuny  E. JamesFermi  W.
JangTexasArlington  B. JargowskyCalIrvine  F. JedinyCzechTechnical  D.
JenaFermi  Y. JeongChungAng,Iowa  C. Jesús-VallsIFAE  X. JiBrookhaven  L.
JiangVirginiaTech  S. JiménezCIEMAT  A. JipaBucharest  R. JohnsonCincinnati
W. JohnsonSouthDakotaSchool  N. JohnstonIndiana  B. JonesTexasArlington  S.
JonesUniversityCollegeLondon  M. JudahPitt  C. JungStonyBrook  T. JunkFermi
Y. JwaColumbia  M. KabirnezhadOxford  A. KabothRoyalholloway,Rutherford  I.
KadenkoKyiv  I. KakorinJINR  A. KalitkinaJINR  D. KalraColumbia  F.
KamiyaFederaldoABC  N. KaneshigeCalSantabarbara  D. M. KaplanIllinoisinstitute
G. KaragiorgiColumbia  G. KaramanIowa  A. KarcherLawrenceBerkeley  M.
KarolakCEASaclay  Y. KaryotakisDannecyleVieux  S. KasaiKure  S. P.
KasettiLouisanastate  L. KashurColoradoState  N. KazaryanYerevan  E.
KearnsBoston  P. KeenerPenn  K. J. KellyCERN  E. KempCampinas  O.
KemulariaGeorgian  W. KetchumFermi  S. H. KettellBrookhaven  M. KhabibullinINR
A. KhotjantsevINR  A. KhvedelidzeGeorgian  D. KimTexasAMcollege  B. KingFermi
B. KirbyColumbia  M. KirbyFermi  J. KleinPenn  A. KlustovaImperial  T.
KobilarcikFermi  K. KoehlerWisconsin  L. W. KoernerHouston  D. H. KohSLAC  S.
KohnCalBerkeley,LawrenceBerkeley  P. P. KollerBern  L. KolupaevaJINR  D.
KorablevJINR  M. KordoskyWilliamMary  T. KoscGrenoble  U. KoseCERN  V.
KosteleckyIndiana  K. KothekarBristol  R. KralikSussex  L. KreczkoBristol  F.
KrennrichIowaState  I. KresloBern  W. KroppCalIrvine  T. KroupovaPenn  S.
KubotaHarvard  Y. KudenkoINR  V. A. KudryavtsevSheffield  S. KulaginINR  J.
KumarHawaii  P. KumarSheffield  P. KunzeDannecyleVieux  N. KuritaSLAC  C.
KuruppuSouthcarolina  V. KusCzechTechnical  T. KutterLouisanastate  J.
KvasnickaCzechAcademyofSciences  D. KwakUNIST  A. LambertLawrenceBerkeley  B.
LandPenn  C. E. LaneDrexel  K. LangTexasaustin  T. LangfordYale  M.
LangstaffManchester  J. LarkinBrookhaven  P. LasorakSussex  D. LastPenn  A.
LaundrieWisconsin  G. LaurentiINFNBologna  A. LawrenceLawrenceBerkeley  I.
LazanuBucharest  R. LaZurColoradoState  M. LazzaroniINFNMilano,MilanoUniv  T.
LeTufts  S. LeardiniIGFAE  J. LearnedHawaii  P. LeBrunIPLyon  T. LeCompteSLAC
C. LeeFermi  S. LeeJeonbuk  G. Lehmann MiottoCERN  R. LehnertIndiana  M.
Leigui de OliveiraFederaldoABC  M. LeitnerLawrenceBerkeley  L. M.
LepinManchester  S. LiSLAC  Y. LiBrookhaven  H. LiaoKansasstate  C.
LinLawrenceBerkeley  Q. LinSLAC  S. LinLouisanastate  R. A. LinerosCatolica
J. LingSunyatsen  A. ListerWisconsin  B. R. LittlejohnIllinoisinstitute  J.
LiuCalIrvine  Y. LiuChicago  S. LockwitzFermi  T. LoewLawrenceBerkeley  M.
LokajicekCzechAcademyofSciences  I. LomidzeGeorgian  K. LongImperial  T.
LordWarwick  J. LoSeccoNotreDame  W. C. LouisLosAlmos  X. LuWarwick  K.
LukCalBerkeley,LawrenceBerkeley  B. LundayPenn  X. LuoCalSantabarbara  E.
LuppiINFNFerrara,Ferrarauniv  T. LuxIFAE  V. P. LuzioFederaldoABC  J.
MaalmiParissaclay  D. MacFarlaneSLAC  A. MachadoCampinas  P. MachadoFermi  C.
MaciasIndiana  J. MacierFermi  A. MaddalenaGranSassoLab  A. MaderaCERN  P.
MadiganCalBerkeley,LawrenceBerkeley  S. MagillArgonne  K. MahnMichiganstate
A. MaioLIP,FCULport  A. MajorDuke  J. A. MaloneyDakotaState  G.
MandrioliINFNBologna  R. C. MandujanoCalIrvine  J. C. ManeiraLIP,FCULport  L.
ManentiUniversityCollegeLondon  S. ManlyRochester  A. MannTufts  K.
ManolopoulosRutherford  M. Manrique PlataIndiana  V. N. ManyamBrookhaven  L.
ManzanillasParissaclay  M. MarchanFermi  A. MarchionniFermi  W.
MarcianoBrookhaven  D. MarfatiaHawaii  C. MarianiVirginiaTech  J.
MaricicHawaii  R. MarieParissaclay  F. MarinhoFederaldeSaoCarlos  A. D.
MarinoColoradoBoulder  D. MarsdenManchester  M. MarshakMinntwin  C.
MarshallRochester  J. MarshallWarwick  J. MarteauIPLyon  J. Martin-AlboIFIC
N. MartinezKansasstate  D. A. Martinez CaicedoSouthDakotaSchool  P. Martínez
MiravéIFIC  S. MartynenkoStonyBrook  V. MascagnaINFNMilanBicocca,Insubria  K.
MasonTufts  A. MastbaumRutgers  F. MatichardLawrenceBerkeley  S. MatsunoHawaii
J. MatthewsLouisanastate  C. MaugerPenn  N. MauriINFNBologna,BolognaUniversity
K. MavrokoridisLiverpool  I. MawbyWarwick  R. MazzaINFNMilanBicocca  A.
MazzacaneFermi  E. MazzucatoCEASaclay  T. McAskillWellesley  E. McCluskeyFermi
N. McConkeyManchester  K. S. McFarlandRochester  C. McGrewStonyBrook  A.
McNabManchester  A. MefodievINR  P. MehtaJawaharlal  P. MelasAthens  O.
MenaIFIC  H. MendezPuertoRico  P. MendezCERN  D. P. MéndezBrookhaven  A.
MenegolliINFNPavia,Pavia  G. MengINFNPadova  M. MessierIndiana  W.
MetcalfLouisanastate  T. MettlerBern  M. MewesIndiana  H. MeyerWichita  T.
MiaoFermi  G. MichnaSouthDakotaState  T. MiedemaNikhef,Radboud  V.
MikolaUniversityCollegeLondon  R. MilincicHawaii  G. MillerManchester  W.
MillerMinntwin  J. MillsTufts  O. MineevINR  A.
MinottiINFNMilano,MilanoBicocca  O. G. MirandaCinvestav  S. MiryalaBrookhaven
C. MishraFermi  S. MishraSouthcarolina  A. MislivecMinntwin  M.
MitchellLouisanastate  D. MladenovCERN  I. MocioiuPennState  K. MoffatDurham
N. MoggiINFNBologna,BolognaUniversity  R. MohantaHyderabad  T. A. MohayaiFermi
N. MokhovFermi  J. A. MolinaAsuncion  L. Molina BuenoIFIC  E.
MontagnaINFNBologna,BolognaUniversity  A. MontanariINFNBologna  C.
MontanariINFNPavia,Fermi,Pavia  D. MontanariFermi  L. M. Montano
ZetinaCinvestav  S. MoonUNIST  M. MooneyColoradoState  A. F. MoorCambridge  D.
MorenoAntonioNarino  D. MorettiINFNMilanBicocca  C. MorrisHouston  C.
MosseyFermi  M. MoteLouisanastate  E. MotukUniversityCollegeLondon  C. A.
MouraFederaldoABC  J. MousseauMichigan  G. MousterLancaster  W. MuFermi  L.
MualemCaltech  J. MuellerColoradoState  M. MuetherWichita  S. MufsonIndiana
F. MuheimEdinburgh  A. MuirDaresbury  M. MulhearnCalDavis  D. MunfordHouston
H. MuramatsuMinntwin  S. MurphyETH  J. MusserIndiana  J. NachtmanIowa  S.
NaguLucknow  M. NalbandyanYerevan  R. NandakumarRutherford  D. NaplesPitt  S.
NaritaIwate  A. NathIndGuwahati  A. Navrer-AgassonManchester  N.
NayakCalIrvine  M. Nebot-GuinotEdinburgh  K. NegishiIwate  J. K.
NelsonWilliamMary  J. NesbitWisconsin  M. NessiCERN  D. NewboldRutherford  M.
NewcomerPenn  H. NewtonDaresbury  R. NicholUniversityCollegeLondon  F.
Nicolas-ArnaldosGranada  A. NikolicaPenn  E. NinerFermi  K. NishimuraHawaii
A. NormanFermi  A. NorrickFermi  R. NorthropChicago  P. NovellaIFIC  J. A.
NowakLancaster  M. OberlingArgonne  J. Ochoa-RicouxCalIrvine  A.
OlivierRochester  A. OlshevskiyJINR  Y. OnelIowa  Y. OnishchukKyiv  J.
OttCalIrvine  L. PaganiCalDavis  G. PalacioEIA  O. PalamaraFermi  S.
PalestiniCERN  J. M. PaleyFermi  M. PallaviciniINFNGenova,Genova  C.
PalomaresCIEMAT  W. Panduro VazquezRoyalholloway  E. PanticCalDavis  V.
PaolonePitt  V. PapadimitriouFermi  R. PapaleoINFNSud  A. PapanestisRutherford
S. ParamesvaranBristol  S. ParkeFermi  E.
ParozziINFNMilanBicocca,MilanoBicocca  Z. ParsaBrookhaven  M. ParvuBucharest
S. PascoliDurham,BolognaUniversity  L. PasqualiniINFNBologna,BolognaUniversity
J. PasternakImperial  J. PaterManchester  C. PatrickUniversityCollegeLondon
L. PatriziiINFNBologna  R. B. PattersonCaltech  S. PattonLawrenceBerkeley  T.
PatzakParisuniversite  A. PaudelFermi  B. PaulosWisconsin  L.
PaulucciFederaldoABC  Z. PavlovicFermi  G. PawloskiMinntwin  D. PayneLiverpool
V. PecCzechAcademyofSciences  S. J. PeetersSussex  A. Pena PerezSLAC  E.
PennacchioIPLyon  A. PenzoIowa  O. L. PeresCampinas  J. PerryEdinburgh  D.
PersheyDuke  G. PessinaINFNMilanBicocca  G. PetrilloSLAC  C.
PettaINFNCatania,CataniaUniversitadi  R. PettiSouthcarolina  V.
PiaINFNBologna,BolognaUniversity  F. PiastraBern  L. PickeringMichiganstate
F. PietropaoloCERN,INFNPadova  V. L. PimentelCti,Campinas  G.
PinaroliBrookhaven  K. PlowsOxford  R. PlunkettFermi  R. PolingMinntwin  F.
PompaIFIC  X. PonsCERN  N. PoonthottathilIowaState  F.
PoppiINFNBologna,BolognaUniversity  S. PordesFermi  J. PorterSussex  M.
PotekhinBrookhaven  R. PotenzaINFNCatania,CataniaUniversitadi  B. V.
PotukuchiJammu  J. PozimskiImperial  M. PozzatoINFNBologna,BolognaUniversity
S. PrakashCampinas  T. PrakashLawrenceBerkeley  M. PrestINFNMilanBicocca  S.
PrinceHarvard  F. PsihasFermi  D. PugnereIPLyon  X. QianBrookhaven  J.
RaafFermi  V. RadekaBrookhaven  J. RademackerBristol  B. RadicsETH  A.
RafiqueArgonne  E. RaguzinBrookhaven  M. RaiWarwick  M. RajaoalisoaCincinnati
I. RakhnoFermi  A. RakotonandrasanaAntananarivo  L.
RakotondravohitraAntananarivo  R. RameikaFermi  M. Ramirez DelgadoPenn  B.
RamsonFermi  A. RappoldiINFNPavia,Pavia  G. RaselliINFNPavia,Pavia  P.
RatoffLancaster  S. RautStonyBrook  R. RazakamiandraAntananarivo  E.
ReaMinntwin  J. RealGrenoble  B. RebelWisconsin,Fermi  R. RechenmacherFermi
M. Reggiani-GuzzoManchester  J. ReichenbacherSouthDakotaSchool  S. D.
ReitznerFermi  H. Rejeb SfarCERN  A. RenshawHouston  S. ResciaBrookhaven  F.
ResnatiCERN  A. ReynoldsOxford  M. RibasTecnologica  S. RiboldiINFNMilano  C.
RiccioStonyBrook  G. RiccobeneINFNSud  L. C. RicePitt  J. RicolGrenoble  A.
RigamontiCERN  Y. RigautETH  E. V. RincónEIA  H. Ritchie-YatesRoyalholloway
D. RiveraLosAlmos  A. RobertGrenoble  L. RochesterSLAC  M. RodaLiverpool  P.
RodriguesOxford  M. J. Rodriguez AlonsoCERN  E. Rodriguez BonillaAntonioNarino
J. Rodriguez RondonSouthDakotaSchool  S. Rosauro-AlcarazMadrid  M.
RosenbergPitt  P. RosierParissaclay  B. RoskovecCalIrvine  M.
RossellaINFNPavia,Pavia  M. RossiCERN  J. RoutJawaharlal  P. RoyWichita  A.
RubbiaETH  C. RubbiaGranSasso  B. RussellLawrenceBerkeley  D.
RuterboriesRochester  A. RybnikovJINR  A. Saa-HernandezIGFAE  R.
SaakyanUniversityCollegeLondon  S. SacerdotiParisuniversite  T.
SaffordMichiganstate  N. SahuIndHyderabad  P. SalaINFNMilano,CERN  N.
SamiosBrookhaven  O. SamoylovJINR  M. SanchezIowaState  V. SandbergLosAlmos
D. A. SandersMississippi  D. SankeyRutherford  S. SantanaPuertoRico  M.
Santos-MaldonadoPuertoRico  N. SaoulidouAthens  P. SapienzaINFNSud  C.
SarastyCincinnati  I. SarcevicArizona  G. SavageFermi  V. SavinovPitt  A.
ScaramelliINFNPavia  A. ScarffSheffield  A. ScarpelliBrookhaven  T.
SchefkeLouisanastate  H. SchellmanOregonState,Fermi  S.
SchifanoINFNFerrara,Ferrarauniv  P. SchlabachFermi  D. SchmitzChicago  A. W.
SchneiderMassinsttech  K. ScholbergDuke  A. SchukraftFermi  E. SegretoCampinas
A. SelyuninJINR  C. R. Senise Jr.Unifesp  J. SensenigPenn  A. SergiBirmingham
D. SgalabernaETH  M. ShaevitzColumbia  S. ShafaqJawaharlal  F. ShakerYork  M.
ShammaCalRiverside  R. SharankovaTufts  H. R. SharmaJammu  R. SharmaBrookhaven
R. K. SharmaPunjab  T. ShawFermi  K. ShchabloIPLyon  C. Shepherd-
ThemistocleousRutherford  A. SheshukovJINR  S. ShinJeonbuk  I.
ShoemakerVirginiaTech  D. ShooltzMichiganstate  R. ShrockStonyBrook  H.
SiegelColumbia  L. SimardParissaclay  J. SinclairSLAC  G.
SinevSouthDakotaSchool  J. SinghLucknow  J. SinghLucknow  L. SinghCUSB  P.
SinghQMUL  V. SinghCUSB,Banaras  R. SiposCERN  F. SippachColumbia  G.
SirriINFNBologna  A. SitrakaSouthDakotaSchool  K. SiyeonChungAng  K.
SkarpaasSLAC  A. SmithCambridge  E. SmithIndiana  P. SmithIndiana  J.
SmolikCzechTechnical  M. SmyCalIrvine  E. SniderFermi  P.
SnopokIllinoisinstitute  D. Snowden-IfftOccidental  M. Soares NunesSyracuse
H. SobelCalIrvine  M. SoderbergSyracuse  S. SokolovJINR  C. J. Solano
SalinasIngenieria  S. Söldner-RemboldManchester  S. SoletiLawrenceBerkeley  N.
SolomeyWichita  V. SolovovLIP  W. E. SondheimLosAlmos  M. SorelIFIC  A.
SotnikovJINR  J. Soto-OtonCIEMAT  F. Soto UgaldiIngenieria  A. SousaCincinnati
K. SoustruznikCharles  F. SpagliardiOxford  M.
SpanuINFNMilanBicocca,MilanoBicocca  J. SpitzMichigan  N. J. C.
SpoonerSheffield  K. SpurgeonSyracuse  M. StancariFermi  L.
StancoINFNPadova,Padova  C. StanfordHarvard  D. StefanCERN  R. SteinBristol
H. SteinerLawrenceBerkeley  A. F. Steklain LisbôaTecnologica  J.
StewartBrookhaven  B. StillwellChicago  J. StockSouthDakotaSchool  F.
StockerCERN  T. StokesLouisanastate  M. StraitMinntwin  T. StraussFermi  L.
StrigariTexasAMcollege  A. StuartColima  J. G. SuarezEIA  J. Suárez
SunciónIngenieria  R. SulejFermi,Otwock  H. SullivanTexasArlington  D.
SummersMississippi  A. SurdoINFNLecce  V. SusicBasel  L. SuterFermi  C.
SuteraINFNCatania,CataniaUniversitadi  R. SvobodaCalDavis  B.
SzczerbinskaTexasAMcorpuscristi  A. M. SzelcEdinburgh  H. TanakaSLAC  S.
TangBrookhaven  A. TapiaMedellin  B. Tapia OreguiTexasaustin  A.
TapperImperial  S. TariqFermi  E. TarparaBrookhaven  N. TataHarvard  E.
TatarIdaho  R. TayloeIndiana  A. TekluStonyBrook  P.
TennessenLawrenceBerkeley,Antalya  M. TentiINFNBologna  K. TeraoSLAC  C. A.
TernesIFIC  F. TerranovaINFNMilanBicocca,MilanoBicocca  G. TesteraINFNGenova
T. ThakoreCincinnati  A. TheaRutherford  J. L. ThompsonSheffield  C.
ThornBrookhaven  S. TimmFermi  V. TishchenkoBrookhaven  L.
TomassettiINFNFerrara,Ferrarauniv  A. TonazzoParisuniversite  D.
TorbunovMinntwin  M. TortiINFNMilanBicocca,MilanoBicocca  M. TortolaIFIC  F.
TortoriciINFNCatania,CataniaUniversitadi  N. TosiINFNBologna  D.
TotaniCalSantabarbara  M. ToupsFermi  C. TouramanisLiverpool  R.
TravagliniINFNBologna  J. TrevorCaltech  S. TrilovBristol  W. H.
TrzaskaJyvaskyla  Y. TsaiCalIrvine  Y. TsaiSLAC  Z. TsamalaidzeGeorgian  K.
TsangSLAC  N. TsveravaGeorgian  S. TufanliCERN  C. TullLawrenceBerkeley  E.
TyleySheffield  M. TzanovLouisanastate  L. UboldiCERN  M. A. UchidaCambridge
J. UrheimIndiana  T. UsherSLAC  S. UzunyanNorthernillinois  M. R. VaginsKavli
P. VahleWilliamMary  S. ValderSussex  G. A. ValdiviessoFederaldeAlfenas  E.
ValenciaGuanajuato  R. ValentimUnifesp  Z. VallariCaltech  E.
VallazzaINFNMilanBicocca  J. W. ValleIFIC  S. VallecorsaCERN  R. Van BergPenn
R. G. Van de WaterLosAlmos  D. Vanegas ForeroMedellin  D. VanneromMassinsttech
F. VaraniniINFNPadova  D. VargasIFAE  G. VarnerHawaii  J. VaselIndiana  S.
VasinaJINR  G. VasseurCEASaclay  N. VaughanOregonState  K. VaziriFermi  S.
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WangCalLosangeles  J. WangSouthDakotaSchool  L. WangLawrenceBerkeley  M. H.
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WawrowskaRutherford,Sussex  P. WeatherlyDrexel  A. WeberMainz,Fermi  M.
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YangStonyBrook  K. YangOxford  T. YangFermi  A. YankelevichCalIrvine  N.
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ZeugWisconsin  C. ZhangBrookhaven  S. ZhangIndiana  Y. ZhangPitt  M.
ZhaoBrookhaven  E. ZhivunBrookhaven  G. ZhuOhiostate  E. D.
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ZuklinCzechAcademyofSciences  V. ZutshiNorthernillinois  R. ZwaskaFermi

###### Abstract

Liquid argon time projection chamber detector technology provides high spatial
and calorimetric resolutions on the charged particles traversing liquid argon.
As a result, the technology has been used in a number of recent neutrino
experiments, and is the technology of choice for the Deep Underground Neutrino
Experiment (DUNE). In order to perform high precision measurements of
neutrinos in the detector, final state particles need to be effectively
identified, and their energy accurately reconstructed. This article proposes
an algorithm based on a convolutional neural network to perform the
classification of energy deposits and reconstructed particles as track-like or
arising from electromagnetic cascades. Results from testing the algorithm on
experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are
presented. The network identifies track– and shower–like particles, as well as
Michel electrons, with high efficiency. The performance of the algorithm is
consistent between experimental data and simulation.

††journal: Eur. Phys. J. C

##  1 Introduction

The ProtoDUNE single phase detector (ProtoDUNE-SP) Abi:2020mwi ; DUNE:2021hwx
is a prototype liquid argon time projection chamber (LArTPC) for the Deep
Underground Neutrino Experiment (DUNE) far detector Abi:2020wmh ;
dunetdr_volume4 . ProtoDUNE-SP is known as a single phase detector as it is
operated entirely within liquid phase argon. The detector readout mechanism
consists of six Anode Plane Assemblies (APAs), each containing three wire
readout planes at angles of $\pm 36^{\circ}$ and $0^{\circ}$ to the vertical,
where the readout planes are denoted U, V and W, respectively. The U and V
views are the induction views, meaning that charge is induced on the wires by
drifting electrons, and the W-view wires collect the drifting electrons. The
wires in each readout plane are spaced with approximately 5 mm pitch and are
read out at a rate of 2 MHz. A full description of the detector is given in
Ref. DUNE:2021hwx . ProtoDUNE-SP collected data from a positively-charged-
particle beam at CERN PhysRevAccelBeams.20.111001 ;
PhysRevAccelBeams.22.061003  in autumn 2018, including charged pions, charged
kaons, protons, muons and positrons recorded with momenta in the range from
0.3 to 7.0 GeV/c. Additionally, since ProtoDUNE-SP is located on the Earth’s
surface, it is subject to a large flux of cosmic ray muons.

The particle interactions can be easily and naturally visualised as three two-
dimensional images (one for each readout view) in the wire number and time
parameter space. Each pixel in the image represents the measured charge from a
reconstructed energy deposition, called a _hit_ , on a given wire at a given
time. A major challenge in the automated reconstruction of particle
interactions in LArTPCs is identifying whether energy deposits originate from
track-like (linear, such as protons, charged kaons, charged pions, and muons)
or shower-like (locally dense, such as electrons and photons) structures. An
example of a 7 GeV/$c$ charged pion interaction is given in Fig. 1, where the
$\pi^{+}$ enters the detector and interacts (just after wire 200 and at time
tick 4500) producing a number of track– and shower–like particles. In order to
classify the interaction type of the $\pi^{+}$, for example as charge-exchange
or inelastic scattering, the particles emitted from the interaction vertex
must be identified. The classification of reconstructed particles as track-
like or shower-like will also be important in DUNE for the correct
classification of neutrino interactions in the far detector. The
identification of Michel electrons helps to distinguish between $\mu^{+}$ and
$\mu^{-}$. It can also be used to identify stopping charged pions whose energy
can be fully reconstructed.

Figure 1: A 7 GeV/$c$ beam $\pi^{+}$ interaction in the collection view
(W-view) in ProtoDUNE-SP data. The x axis shows the wire number. The y axis
shows the time tick in the unit of 0.5 $\mu$s. The colour scale represents the
charge deposition.

In this article, we propose and demonstrate the use of a convolutional neural
network (CNN) to classify hits as either belonging to track–like or
shower–like structures aidanthesis . Furthermore, a Michel electron score is
given to each hit to help identify Michel electrons and positrons, produced in
the decay of muons and antimuons, respectively Michel:1949qe 111Michel
electron will be used to refer to both Michel electrons and Michel positrons..
These hit-level classifications can be used alongside pattern recognition
based reconstruction algorithms such as Pandora pandorasdk ; pandora  to
refine the track or shower classification of reconstructed particles. The
performance of the Pandora reconstruction on ProtoDUNE-SP simulated and
experimental data is described in detail in Ref. pandoraProtoDUNE .
Convolutional neural networks have been successfully used in neutrino physics
for event classification novacvn ; microboonecvn ; dunecvn , particle
identification microbooneparticle ; novaparticle  and reconstruction
Abbasi:2021ryj . The fine-grain images obtained from LArTPC detectors makes
CNNs a natural choice for such tasks. This algorithm is novel in that it aims
to classify the hits based on a small local neighbourhood as opposed to a
semantic segmentation approach that uses a much larger image containing a
large part (or all) of the detector. The algorithm was designed in this way to
minimise the memory usage and computational processing time, allowing it to
run quickly on standard computing node CPUs where there is no access to
powerful GPUs.

##  2 The convolutional neural network

Convolutional neural networks extract features from images by applying a
series of filters that are learned during the training process Jackel2008 ;
Szegedy2015 . The number of filters and the number of convolutional layers
varies for each specific use case; they are determined by the class of problem
the network is trying to solve, and the computer hardware available for
training and evaluating the network. In this case, a GPU was available for the
training of the network, but the inference is performed on computing cluster
CPUs (where GPUs are typically not available) as a part of the ProtoDUNE-SP
reconstruction chain. As a result, only simple architectures containing few
convolutional layers were considered, constrained by the desired evaluation
time on the CPUs. For inference tasks within the ProtoDUNE-SP event
reconstruction workflow, a C++ interface was added to the LArSoft framework
Church:2013hea . Recent attempts to introduce GPU acceleration into the
workflow mentioned above show promising reductions in processing time
GPUasaservice .

The input to the network is a small region of the entire detector image known
as a _patch_. For each reconstructed hit object, the wire number $w$ and peak
time $t$ are extracted, and a $48\times 48$ pixel image, centred on
$\left(w,t\right)$, is created and the value of each pixel corresponds to the
detected charge on a given wire at a given time. The wire dimension of the
image corresponds to 48 wires with one wire per pixel. The time data are
downsampled by averaging over six time samples, such that the spatial
dimensions of the pixels match the 5 mm wire pitch in both directions.
Therefore, each image represents around $24\times 24$ cm2 of wire data. Figure
2 shows the hits from one APA in a simulated ProtoDUNE-SP event and the three
zoomed regions give example $48\times 48$ pixel patches in the track, shower
and Michel categories. Detector effects such as the ones introduced by space
charge Abi:2020mwi ; Palestini:2021mlc  are included in the simulation. The
images from the three wire planes are independently evaluated. This paper only
reports on results from the collection plane, which has the highest signal-to-
noise ratio.

Figure 2: Examples of CNN input patches from a simulated ProtoDUNE-SP event.
The inputs to the CNN are small $48\times 48$ pixel images created from
patches of the full detector readout. Three examples are shown, each labelled
with their appropriate class. The patch of the detector readout from which
each patch was generated is emphasised.

The architecture for this hit-tagging CNN is shown in Fig. 3. A single
convolutional layer containing 48 $5\times 5$ pixel filters is used to extract
feature maps from the input image, which are then flattened and passed to two
dense layers that use them to classify the images. Two dropout222Dropout
randomly disables a given fraction of neurons for each training example.
layers are used for regularisation dropout . The output of the network is
split into two branches. The first branch returns the scores for track,
shower, or empty (TSE) classification, which can be interpreted as
probabilities as they are constrained to sum to one by a softmax
nwankpa2018activation  activation function. The second returns the probability
for a Michel electron classification, with a sigmoid nwankpa2018activation
activation function. The output of the network is split in this way due to the
overlap of the shower and Michel electron classes. The total loss function is
a weighted sum of the two branches, with the weights derived from the relative
size of the training samples in each branch.

Figure 3: The CNN architecture. In this case, the CNN processes 256 images in
parallel. Each image is a $48\times 48$ pixel patch of the calibrated detector
readout. A single convolutional layer, with 48 filters of size $5\times 5$, is
used to extract features from the images. These are processed by two dense
layers containing 128 and 32 neurons respectively, before being split into two
branches which provide the track-shower-empty (TSE) and Michel outputs. The
dimensions of the data after each operation are given next to the black
arrows.

###  2.1 Training details

For the purposes of training a true classification must be attached to each of
the patches. In addition to track, shower and Michel electron patches, empty
patches are also created where the central pixel contains no energy deposit.
Approximately 30 million images were prepared in total using approximately 500
simulated events: $\sim$15 million in the track sample, $\sim$11 million in
the shower sample, $\sim$3 million in the empty sample, and $\sim$1 million in
the Michel electron sample.

The CNN was trained with TensorFlow Abadi-et-al-2016-tensorflow  through its
Keras chollet2015keras  interface, and performance metrics, such as the
losses, purity and efficiency, were monitored throughout training using
TensorBoard tensorboard . Before training, the data set was split into
training, test, and validation sets in the ratio 80:10:10. The performance
metrics were monitored throughout training with the training and validation
sets, and again after training with the test set. Figure 4 shows the evolution
of the training and validation losses throughout the training. Due to the
large number of training images and relative simplicity of the task, the
losses fall sharply within the first epoch, which is not visible in the plots.
The training and validation losses begin to diverge after the first few epochs
suggesting there is some over-fitting, but the network generalises well when
considering the similar performance of the algorithm on the test and
validation sets. To further ensure generalisation, an early stopping algorithm
was used, which focused on the loss in the TSE branch Prechelt2012 . The final
weights for the network were taken from a checkpoint at the end of the fifth
epoch333An epoch is defined as one iteration over the entire training sample.
since the validation loss in the TSE branch starts to plateau on the fifth
epoch.

Figure 4: Evolution of the training and validation losses as a function of
training epoch. The final weights of the network were taken from a checkpoint
at the end of the fifth epoch, shown here as a vertical line. The overall
loss; track, shower and empty loss; and Michel loss are shown in the top left,
top right, and bottom left respectively. In calculating the overall loss, the
track, shower and empty loss is weighted by 0.1 to be consistent with the
smaller size of the Michel sample.

###  2.2 Performance

The performance of the hit tagging was evaluated with reconstructed events
from ProtoDUNE-SP simulation. A $48\times 48$ pixel image is created around
each reconstructed hit, which is then classified by the network and the
classification compared with the truth label. Note that by definition this
method ensures that no processing is performed on empty images. Figure 5 shows
the shower score distributions for true shower hits and all other hits, and a
strong separation is seen between the distributions with a score close to one
corresponding to a hit that is highly likely to come from a shower. The small
peak in the other hits distribution close to one comes from delta-ray
electrons overlapping with the cosmic-ray muon that produced them. The
classification threshold can be set on a case by case basis, for the initial
validation of the network on the ProtoDUNE-SP data it was optimised based on
the F1 score, which is given by:

$\frac{1}{F_{1}}=\frac{1}{2}\left(\frac{1}{\textrm{purity}}+\frac{1}{\textrm{efficiency}}\right),$
(1)

where the purity is defined as the fraction of correctly classified shower
hits in the sample of all selected shower hits, and the efficiency as the
fraction of all true shower hits that were selected as shower hits.

Figure 5: Shower classifier output distributions. The output of the shower
classifier is shown for true shower hits in red and all other hits in blue.
The blue line shows the F1 score as a function of classification threshold.

Figure 6 demonstrates the performance of the network in terms of the true
positive and false positive rates. In this case, the true positive rate is the
fraction of true shower hits that have been correctly classified as shower
hits, and the false positive rate is the fraction of other hits incorrectly
classified as shower hits. The receiver operating characteristic (ROC) curve
is shown, which shows the true positive rate against the false positive rate
as the selection threshold on the shower classifier output is varied. ROC
curves are shown for simulation with the space charge effect (SCE, red) and
without (blue). The close agreement between the curves suggests that the CNN
results are robust against changes in the SCE model.

Figure 6: ROC curves for the shower classifier, showing the true positive rate
against false the positive rate for varying classification threshold on the
shower classifier output. The red (blue) line shows the ROC curve from
ProtoDUNE-SP simulation with (without) SCE. The red curve is obscured by the
blue due to close agreement.

The score distributions from the Michel electron classifier are shown in Fig.
7, for true Michel electron hits and all other hits. The Michel electron
classification is a significantly more challenging problem, partly due to the
large variation in Michel electron interactions in the detector. Michel
electrons can be seen as single short track-like objects, or more fragmented
due to photon emission and subsequent Compton scattering to produce additional
electrons. Furthermore, some delta-ray electrons and components of
electromagnetic showers can produce signatures in the detector that are
similar to Michel electrons. Therefore, while both distributions are strongly
peaked at the expected values, with Michel electrons close to one and other
hits close to zero, there are also sub-leading peaks of hits that are not
correctly classified. Due to this, and the significantly smaller sample of
Michel electron hits, the network is not able to achieve a good performance in
terms of the F1 metric. However, when combined with simple clustering, a high
purity sample of Michel electron events can be selected, as will be discussed
in Sec. 3.

Figure 7: Michel electron classifier output distributions.

##  3 Results from experimental data and simulation

It is important that the CNN is robust against potential differences between
experimental data and simulation, and hence the performance has been compared
between experimental data and simulation for several particle species. Hits
are tagged in the three different readout views and reconstructed particles
from Pandora are assigned a score between 0 and 1 that is the average of the
shower classifier score from the CNN from all of the 2D hits in the collection
view. Each hit is weighted by the hit charge when calculating the average
shower score. A score close to one means that it is highly probable that the
particle is shower–like, and a low score means the particle is very likely to
be track–like.

Data from ProtoDUNE-SP runs 5387 and 5809 taken in the H4-VLE test beam at
CERN with 1 GeV/$c$ beam momentum were used for the initial qualitative
validation of the CNN performance on ProtoDUNE-SP data. These runs contain
cosmic rays and particles from the charged particle beam. Run 5809 was taken
with the inclusive beam trigger giving a dataset primarily consisting of beam
positrons. Run 5387 was taken with a trigger that vetoed positrons, which
resulted in a sample primarily consisting of beam $\pi^{+}$’s, $\mu^{+}$’s and
protons. Figure 8 shows an example of the CNN shower scores of reconstructed
particles in a ProtoDUNE-SP event as a visual cross-check of the CNN
performance. As expected, the cosmic-ray muon and pion tracks in the event
have low shower scores, while the photon shower from the charged particle beam
interaction is given a high score. In addition, delta ray electrons, which are
emitted along the muon tracks, are associated with showers and therefore
receive a high CNN shower score. The latest ProtoDUNE-SP Monte Carlo (MC)
simulation sample was used to compare with experimental data. This is a new MC
simulation with improved modelling of detector response, which is completely
independent of the previous MC simulation that was used to train the CNN.

Figure 8: The CNN shower score of each hit in reconstructed particles for the
same 7 GeV/$c$ $\pi^{+}$ interaction shown in Fig. 1. This diagram shows the
location of reconstructed hits in wire–time coordinates, and the hits are
coloured based on the CNN shower score. Red hits are track–like, and blue hits
are shower–like. A number of cosmic muon tracks can be seen, along with tracks
and showers produced by the pion interaction. The small shower–like patches
along the muon tracks are delta-ray electrons.

The following sections report the performance of the CNN classification at the
hit level and the particle level for cosmic rays and charged particles from
the test beam. In order to classify the hits, a threshold of 0.72 was applied
to the shower classifier output of the CNN, with hits exceeding the threshold
being classified as shower hits. This threshold was selected by choosing the
value with the largest F1 score in Fig. 5. For particle-level classification,
a different threshold of 0.81 is applied to the average shower score to
classify particles, where the threshold was chosen to maximise the product of
the selection efficiencies of all four types of charged beam particles.

###  3.1 Cosmic-ray muons

The CNN was first tested on a sample of cosmic-ray muons444Since ProtoDUNE-SP
does not have a magnetic field this sample also contains antimuons. in order
to validate its performance before it was used to classify beam particles (see
Sec. 3.2). A sample of cosmic-ray muons was selected from simulation and
experimental data (run 5387), where cosmic-ray muon candidates were selected
using the following criteria:

  * •

the particle was reconstructed by Pandora as a track

  * •

the track was at least 1 m in length

  * •

the track started and ended at least 50 cm from the front face of the detector
(to veto beam particles)

  * •

the track was directed at least $15^{\circ}$ away from the vertical (to veto
tracks that only deposited energy on a small number of collection plane
wires).

All of the hits associated to the selected tracks were labelled as true
cosmic-ray muon hits. The hits from any other particles associated with the
cosmic-ray muon candidate, such as delta-ray and Michel electrons, were not
included to avoid contaminating the hit selection.

Firstly, the hit-level classification was studied. Figure 9 shows the CNN
shower output score for cosmic-ray hits in experimental data (black) and
simulation (red), and demonstrates the high level of agreement between the two
samples. The peak in the score distribution close to one can be attributed to
hits from the numerous delta-ray electrons produced by high energy muons, such
as those shown previously in Fig. 8. The results of the hit-level
classification, obtained by measuring the fraction of hits below a threshold
of 0.72, are given in Table 1.

Figure 9: The CNN shower classifier scores for cosmic-ray muon hits from
experimental data (black) and simulation (red). The error bars on the data are
statistical.

Figure 10 shows the particle-level comparison of the average CNN shower score
for the cosmic-ray muons in experimental data and simulation. As expected, the
distributions are peaked close to zero, and the experimental data distribution
is slightly shifted compared to the simulation. However, when applying the
threshold of 0.81 to classify the cosmic rays as track-like, both the
performance and agreement between experimental data and simulation is
excellent, as shown in Table 1.

Figure 10: The average CNN shower classifier scores for cosmic-ray muons. The error bars on the experimental data are statistical. Table 1: The fraction of correctly classified cosmic-ray muon hits and particles using the CNN measured for experimental data and simulation. The errors represent the statistical uncertainties calculated using the Clopper-Pearson method clopperPearson . Stage | Correctly classified (%) | Data/Simulation
---|---|---
Data | Simulation
Hits | 85.6$\pm 0.0$ | 87.3$\pm 0.0$ | 0.980$\pm 0.000$
Particles | 99.8$\pm 0.1$ | 100.0${}^{+0.0}_{-0.1}$ | 0.998$\pm 0.002$

###  3.2 Charged particle test beam

In the case of particles originating from the charged particle beam in the
experimental data samples, the beam instrumentation Abi:2020mwi  can be used
to provide an effective truth source to which the results from the CNN can be
compared. For simulation samples we use the truth information to get the
primary beam particle species information. This allows the shower score
distributions from the CNN to be compared between experimental data and
simulation for different particle species. The reconstructed particles with
angles inconsistent with the beam direction and that arrive out-of-time with
the beam can be assumed to be cosmic muons. Note that at 1 GeV/$c$ beam
momentum, $\pi^{+}$ and $\mu^{+}$ are indistinguishable using the beam
instrumentation information. A 1 GeV/$c$ $\mu^{+}$ is expected to stop in the
middle of the detector around z = 380 cm, where the z axis is horizontal. A 1
GeV/$c$ $\pi^{+}$ will most likely interact with the argon nucleus before
stopping because of the relatively short interaction length ($\sim$100 cm). We
identify an event as a pion if the reconstructed track end z position is less
than 100 cm and as a muon if the end z position is greater than 300 cm for
events identified by the beam instrumentation as either pions or muons. We
require the number of collection plane hits in the reconstructed shower should
be greater than 200 for the positron candidate events in order to remove
events with an incompletely reconstructed shower. This cut is not applied to
the other three particle species. Table 2 shows the numbers of events after
the beam quality and number of hits cuts for beam pions, muons, protons and
positrons and the purity of the selected samples based on the truth
information in the simulation.

Table 2: Numbers of events after the beam quality and number of hits selection criteria shown for experimental data and simulation. | $\pi^{+}$ | $\mu^{+}$ | $p$ | $e^{+}$
---|---|---|---|---
Data | 5402 | 1228 | 9364 | 9106
Simulation | 16612 | 1305 | 23660 | 42245
Simulation purity | 84.4% | 86.7% | 99.8% | 97.7%

Figure 11 shows the distribution of shower classifier score for each
individual hit in the beam pions, muons, protons, and positrons. The data in
all of the beam particle distributions are normalised by the number of
triggered beam particles of the given flavour after the beam quality and
number of hits cuts. There is a reasonable agreement between the experimental
data and simulation in terms of the shower score distributions for each
particle species. To quantify the efficiency to select track–like and
shower–like hits, Table 3 lists the fraction of individual hits selected into
the appropriate category for each sample in experimental data and simulation
for a selection threshold of 0.72. The difference between the selected
fraction in each case is an estimate of the systematic uncertainty associated
with hit-by-hit selection. The class used for the selection in each sample is
also given in Table 3. The fractional difference between experimental data and
simulation varies based on the particles species, and falls in the range of
1-2%.

(a) pion

(b) muon

(c) proton

(d) positron

Figure 11: Shower classifier scores for different particle species in the ProtoDUNE-SP beam. The error bars on the experimental data are statistical. Table 3: Fraction of hits classified into appropriate class for different samples in ProtoDUNE-SP data and simulation. The statistical uncertainties on the fractions and ratios are negligible. Hit Source | Class | Data Fraction (%) | Simulation Fraction (%) | Data / Simulation
---|---|---|---|---
Pion | Track | 78.7 | 80.3 | 0.98
Muon | Track | 92.7 | 92.0 | 1.01
Proton | Track | 93.0 | 94.5 | 0.98
Positron | Shower | 93.0 | 91.4 | 1.02

Figure 12 shows the distribution of the average shower classifier scores over
all the hits in the reconstructed pion, proton, and positron particles. This
average shower classifier score is what analysers normally use to identify a
reconstructed particle as a track–like or shower–like particle. The
distributions in each category are normalised to unit area. The experimental
data and simulation distributions are in a reasonable agreement. There is a
long tail in the average shower classifier score distribution for both the
beam pions and protons. This tail is caused by the spatial distortion
introduced by the SCE and is largely suppressed if we make the distributions
using simulation sample without simulating SCE. There is a shift in the
average shower classifier score for beam positrons between experimental data
and simulation. There are slightly more hits in experimental data than in
simulation for reconstructed positron events, making the experimental data
hits more shower–like. It can be seen that the score distribution for the beam
muons is more strongly peaked towards low scores than for cosmic-ray muons,
shown in Fig. 10, because they are significantly lower in energy and hence
produce fewer delta rays. Table 4 lists the fraction of reconstructed
particles selected into the appropriate category for each sample in
experimental data and simulation for a selection threshold of 0.81. The
fractional difference between experimental data and simulation is within 1%
for all particles species.

(a) pion

(b) muon

(c) proton

(d) positron

Figure 12: Average shower classifier scores for different particle species in the ProtoDUNE-SP beam. The error bars on the experimental data are statistical. Table 4: Fraction of reconstructed particles classified into appropriate class for different samples in ProtoDUNE-SP data and simulation. The errors represent the statistical uncertainties calculated using the Clopper-Pearson method clopperPearson . Hit Source | Class | Data Fraction (%) | Simulation Fraction (%) | Data / Simulation
---|---|---|---|---
Pion | Track | 91.7$\pm$0.4 | 92.5$\pm$0.2 | 0.991$\pm$0.005
Muon | Track | 100${}^{+0.0}_{-0.1}$ | 100${}^{+0.0}_{-0.1}$ | 1.000${}^{+0.000}_{-0.002}$
Proton | Track | 96.9$\pm$0.2 | 97.1$\pm$0.1 | 0.998$\pm$0.002
Positron | Shower | 98.8$\pm$0.1 | 97.9$\pm$0.1 | 1.010$\pm$0.001

###  3.3 Michel electrons

To validate the performance of the CNN Michel score calculation, we examine
the Michel score of hits around the muon and pion track end point. Hits around
the muon end points are most likely from the Michel electron which are
expected to have a high Michel score. We define a window of 30 wires $\times$
200 ticks (approximately $15\times 16$ cm2) centred around the reconstructed
track end point projected on the collection plane to select daughter hits.
Hits from the secondary particles produced by the pion interaction are
expected to have a low Michel score as shown in Fig. 13(a). The Michel hits
from the muon decay are expected to have a high Michel score as shown in Fig.
13(b). Hits on the primary beam track or on another track that is longer than
25 cm are excluded to remove the contributions from primary beam particle and
cosmic ray muons. Figures 14 and 15 show the hit-level and particle level
comparison of the CNN Michel score over daughter hits in the reconstructed
pion and muon particles.

(a) A pion candidate

(b) A muon candidate

Figure 13: CNN Michel score for reconstructed primary beam particles and
secondary particles in a reconstructed pion (left) and muon (right) particle.
Each coloured pixel shows the peak time and wire number of a hit. The box
surrounding the track end point is used to select the daughter hits. The
average daughter Michel score is 0.005 for the pion event and 1.000 for the
muon event.

(a) Pion candidates

(b) Muon candidates

Figure 14: CNN Michel score for the daughter hits in the 30 wires $\times$ 200
ticks window centred around the reconstructed track end point of the pion
(left) and muon (right) particles.

(a) Pion candidates

(b) Muon candidates

Figure 15: Average CNN Michel score over the daughter hits in the 30 wires
$\times$ 200 ticks window centred around the reconstructed track end point of
the pion (left) and muon (right) particles.

The results of the hit-level and event-level classification, obtained using a
threshold of 0.19, are given in Tables 5 and 6, respectively. The threshold is
chosen to maximise the product of selection efficiencies of pions and muons.
We are able to select 73% of the $\mu^{+}$ events while rejecting 90–92% of
the $\pi^{+}$ events using the average Michel score. The fractional difference
between experimental data and simulation falls in the range of 1–2%. Efficient
identification of Michel electrons provides crucial information on particle
identification and kinematic reconstruction. It allows the separation between
$\mu^{+}$ and $\mu^{-}$ because 70% of the $\mu^{-}$s are captured while most
of the $\mu^{+}$s decay into Michel electrons. It also allows the
identification of stopping $\pi^{+}$ which goes through the decay chain
$\pi^{+}\rightarrow\mu^{+}\rightarrow e^{+}$. The momentum of those stopping
pions can be reconstructed either through track range or using calorimetric
information, which can be used to reconstruct the full kinematics of the final
state particles.

Table 5: Fraction of daughter hits classified into appropriate class for different samples in ProtoDUNE-SP data and simulation. The statistical uncertainties on the fractions and ratios are negligible. Hit Source | Class |  Data Fraction (%) |  Simulation Fraction (%) | Data / Simulation
---|---|---|---|---
Pion daughters | Non-Michel–like | 87.6 | 89.2 | 0.982
Muon daughters | Michel–like | 59.8 | 60.2 | 0.993
Table 6: Fraction of reconstructed particles classified into appropriate class for different samples in ProtoDUNE-SP data and simulation. The errors represent the statistical uncertainties calculated using the Clopper-Pearson method clopperPearson . Hit Source | Class |  Data Fraction (%) |  Simulation Fraction (%) | Data / Simulation
---|---|---|---|---
Pion daughters | Non-Michel–like | 90.4$\pm$0.4 | 92.2$\pm$0.2 | 0.980$\pm$0.005
Muon daughters | Michel–like | 73.2$\pm$1.3 | 72.6$\pm$1.3 | 1.009${}^{+0.025}_{-0.026}$

##  4 Conclusion

In this paper, we described an effective hit tagging algorithm for track,
shower, and Michel electron hit classification based on a convolutional neural
network, using a small patch approach. This algorithm is shown to give good
agreement in selection efficiencies, of around 1–2%, between experimental data
and simulation for cosmic rays and 1 GeV/$c$ test-beam interactions for a hit-
by-hit event selection. When combined with the full event reconstruction
(which includes a BDT-based classifier) and applied to the hits of each
reconstructed particle, the CNN refines the track and shower classification to
produce highly efficient selections that agree within 1% between experimental
data and simulation. Additionally, this network also provides a method to
select Michel electrons, which helps with the particle identification and
kinematic reconstruction. This algorithm is being widely used within ongoing
ProtoDUNE-SP data analyses, including pion cross-section analyses and detector
calibrations.

## Acknowledgements

The ProtoDUNE-SP detector was constructed and operated on the CERN Neutrino
Platform. We gratefully acknowledge the support of the CERN management, and
the CERN EP, BE, TE, EN and IT Departments for NP04/ProtoDUNE-SP. This
document was prepared by the DUNE 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. This work
was supported by CNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, IPP and NSERC,
Canada; CERN; MŠMT, Czech Republic; ERDF, H2020-EU and MSCA, European Union;
CNRS/IN2P3 and CEA, France; INFN, Italy; FCT, Portugal; NRF, South Korea; CAM,
Fundación “La Caixa”, Junta de Andalucía-FEDER, MICINN, and Xunta de Galicia,
Spain; SERI and SNSF, Switzerland; TÜBİTAK, Turkey; The Royal Society and
UKRI/STFC, United Kingdom; DOE and NSF, United States of America. This
research used resources of the National Energy Research Scientific Computing
Center (NERSC), a U.S. Department of Energy Office of Science User Facility
operated under Contract No. DE-AC02-05CH11231.

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