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:  University of Oxford, Oxford, OX1 3RH, United
Kingdom 155155institutetext:  Pacific Northwest National Laboratory, Richland,
WA 99352, USA 156156institutetext:  Universtà degli Studi di Padova, I-35131
Padova, Italy 157157institutetext:  Panjab University, Chandigarh, 160014
U.T., India 158158institutetext:  Université Paris-Saclay, CNRS/IN2P3, IJCLab,
91405 Orsay, France 159159institutetext:  Université de Paris, CNRS,
Astroparticule et Cosmologie, F-75006, Paris, France 160160institutetext:
University of Parma, 43121 Parma PR, Italy 161161institutetext:  Università
degli Studi di Pavia, 27100 Pavia PV, Italy 162162institutetext:  University
of Pennsylvania, Philadelphia, PA 19104, USA 163163institutetext:
Pennsylvania State University, University Park, PA 16802, USA
164164institutetext:  Physical Research Laboratory, Ahmedabad 380 009, India
165165institutetext:  Università di Pisa, I-56127 Pisa, Italy
166166institutetext:  University of Pittsburgh, Pittsburgh, PA 15260, USA
167167institutetext:  Pontificia Universidad Católica del Perú, Lima, Perú
168168institutetext:  University of Puerto Rico, Mayaguez 00681, Puerto Rico,
USA 169169institutetext:  Punjab Agricultural University, Ludhiana 141004,
India 170170institutetext:  Queen Mary University of London, London E1 4NS,
United Kingdom 171171institutetext:  Radboud University, NL-6525 AJ Nijmegen,
Netherlands 172172institutetext:  University of Rochester, Rochester, NY
14627, USA 173173institutetext:  Royal Holloway College London, TW20 0EX,
United Kingdom 174174institutetext:  Rutgers University, Piscataway, NJ,
08854, USA 175175institutetext:  STFC Rutherford Appleton Laboratory, Didcot
OX11 0QX, United Kingdom 176176institutetext:  Università del Salento, 73100
Lecce, Italy 177177institutetext:  San Jose State University, San José, CA
95192-0106, USA 178178institutetext:  Universidad Sergio Arboleda, 11022
Bogotá, Colombia 179179institutetext:  University of Sheffield, Sheffield S3
7RH, United Kingdom 180180institutetext:  SLAC National Accelerator
Laboratory, Menlo Park, CA 94025, USA 181181institutetext:  University of
South Carolina, Columbia, SC 29208, USA 182182institutetext:  South Dakota
School of Mines and Technology, Rapid City, SD 57701, USA 183183institutetext:
South Dakota State University, Brookings, SD 57007, USA 184184institutetext:
Southern Methodist University, Dallas, TX 75275, USA 185185institutetext:
Stony Brook University, SUNY, Stony Brook, NY 11794, USA 186186institutetext:
Sun Yat-Sen University, Guangzhou, 510275 187187institutetext:  Sanford
Underground Research Facility, Lead, SD, 57754, USA 188188institutetext:
University of Sussex, Brighton, BN1 9RH, United Kingdom 189189institutetext:
Syracuse University, Syracuse, NY 13244, USA 190190institutetext:
Universidade Tecnológica Federal do Paraná, Curitiba, Brazil
191191institutetext:  Texas A&M University, College Station, Texas 77840
192192institutetext:  Texas A&M University - Corpus Christi, Corpus Christi,
TX 78412, USA 193193institutetext:  University of Texas at Arlington,
Arlington, TX 76019, USA 194194institutetext:  University of Texas at Austin,
Austin, TX 78712, USA 195195institutetext:  University of Toronto, Toronto,
Ontario M5S 1A1, Canada 196196institutetext:  Tufts University, Medford, MA
02155, USA 197197institutetext:  Universidade Federal de São Paulo, 09913-030,
São Paulo, Brazil 198198institutetext:  Ulsan National Institute of Science
and Technology, Ulsan 689-798, South Korea 199199institutetext:  University
College London, London, WC1E 6BT, United Kingdom 200200institutetext:  Valley
City State University, Valley City, ND 58072, USA 201201institutetext:
Variable Energy Cyclotron Centre, 700 064 West Bengal, India
202202institutetext:  Virginia Tech, Blacksburg, VA 24060, USA
203203institutetext:  University of Warsaw, 02-093 Warsaw, Poland
204204institutetext:  University of Warwick, Coventry CV4 7AL, United Kingdom
205205institutetext:  Wellesley College, Wellesley, MA 02481, USA
206206institutetext:  Wichita State University, Wichita, KS 67260, USA
207207institutetext:  College of William and Mary, Williamsburg, VA 23187, USA
208208institutetext:  University of Wisconsin Madison, Madison, WI 53706, USA
209209institutetext:  Yale University, New Haven, CT 06520, USA
210210institutetext:  Yerevan Institute for Theoretical Physics and Modeling,
Yerevan 0036, Armenia 211211institutetext:  York University, Toronto M3J 1P3,
Canada

# Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase
liquid argon TPC

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  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  P. Barham AlzasCERN
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. B. R. BattatWellesley  F. BattistiOxford  F. BayAntalya  M.
C. Q. BazettoCampinas  J. L. L. 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. J. C. 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. C. 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. C. 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. S. S.
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.
A. B. CoelhoParisuniversite  N. ColtonColoradoState  E. ConleyDuke  R.
ConleySLAC  J. ConradMassinsttech  M. ConverySLAC  S. CopelloINFNGenova  P.
CovaINFNMilano,Parma  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. P.
DecowskiNikhef,Amsterdam  A. De GouvêaNorthwestern  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,Parma  P. De LurgioArgonne  J. R. T. 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. S. DíazIndiana  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. K. GiriIndHyderabad  D. GnaniLawrenceBerkeley  O. GogotaKyiv
M. GoldNewmexico  S. GollapinniLosAlmos  K. GollwitzerFermi  R. A.
GomesFederaldeGoias  L. V. 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  W. 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. 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. A.
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. S. 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. K. 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. A.
KosteleckýIndiana  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. Y. LeeJeonbuk  G. Lehmann MiottoCERN  R. LehnertIndiana  M. A.
Leigui de OliveiraFederaldoABC  M. LeitnerLawrenceBerkeley  L. M.
LepinManchester  S. W. LiSLAC  Y. LiBrookhaven  H. LiaoKansasstate  C. S.
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. M. LoSeccoNotreDame  W. C. LouisLosAlmos  X.-G. LuWarwick  K.
B. LukCalBerkeley,LawrenceBerkeley  B. LundayPenn  X. LuoCalSantabarbara  E.
LuppiINFNFerrara,Ferrarauniv  T. LuxIFAE  V. P. LuzioFederaldoABC  J.
MaalmiParissaclay  D. MacFarlaneSLAC  A. A. MachadoCampinas  P. MachadoFermi
C. T. MaciasIndiana  J. R. 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. 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. Martín-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. D. 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. S. MishraFermi  S. R. MishraSouthcarolina  A. MislivecMinntwin  M.
MitchellLouisanastate  D. MladenovCERN  I. MocioiuPennState  K. MoffatDurham
N. MoggiINFNBologna,BolognaUniversity  R. MohantaHyderabad  T. A. MohayaiFermi
N. MokhovFermi  J. MolinaAsuncion  L. Molina BuenoIFIC  E.
MontagnaINFNBologna,BolognaUniversity  A. MontanariINFNBologna  C.
MontanariINFNPavia,Fermi,Pavia  D. MontanariFermi  L. M. Montaño
ZetinaCinvestav  S. H. 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. J. PattonLawrenceBerkeley
T. PatzakParisuniversite  A. PaudelFermi  B. PaulosWisconsin  L.
PaulucciFederaldoABC  Z. PavlovicFermi  G. PawloskiMinntwin  D. PayneLiverpool
V. PecCzechAcademyofSciences  S. J. M. PeetersSussex  A. Pena PerezSLAC  E.
PennacchioIPLyon  A. PenzoIowa  O. L. G. 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. K. S.
PotukuchiJammu  J. PozimskiImperial  M. PozzatoINFNBologna,BolognaUniversity
S. PrakashCampinas  T. PrakashLawrenceBerkeley  M. PrestINFNMilanBicocca  S.
PrinceHarvard  F. PsihasFermi  D. PugnereIPLyon  X. QianBrookhaven  J. L.
RaafFermi  V. RadekaBrookhaven  J. RademackerBristol  B. RadicsETH  A.
RafiqueArgonne  E. RaguzinBrookhaven  M. RaiWarwick  M. RajaoalisoaCincinnati
I. RakhnoFermi  A. RakotonandrasanaAntananarivo  L.
RakotondravohitraAntananarivo  R. RameikaFermi  M. A. Ramirez DelgadoPenn  B.
RamsonFermi  A. RappoldiINFNPavia,Pavia  G. RaselliINFNPavia,Pavia  P.
RatoffLancaster  S. RautStonyBrook  R. F. RazakamiandraAntananarivo  E. M.
ReaMinntwin  J. S. RealGrenoble  B. RebelWisconsin,Fermi  R. RechenmacherFermi
M. Reggiani-GuzzoManchester  J. ReichenbacherSouthDakotaSchool  S. D.
ReitznerFermi  H. Rejeb SfarCERN  A. RenshawHouston  S. ResciaBrookhaven  F.
ResnatiCERN  M. RibasTecnologica  S. RiboldiINFNMilano  C. RiccioStonyBrook
G. RiccobeneINFNSud  L. C. J. RicePitt  J. S. 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  K. SakashitaKEK  P.
SalaINFNMilano,CERN  N. SamiosBrookhaven  O. SamoylovJINR  M. C.
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. H. 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. W. 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. R. SoletiLawrenceBerkeley
N. SolomeyWichita  V. SolovovLIP  W. E. SondheimLosAlmos  M. SorelIFIC  A.
SotnikovJINR  J. Soto-OtonCIEMAT  F. A. 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  R. SteinBristol  H. M.
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. M. Suárez
SunciónIngenieria  H. SullivanTexasArlington  D. SummersMississippi  A.
SurdoINFNLecce  V. SusicBasel  L. SuterFermi  C. M.
SuteraINFNCatania,CataniaUniversitadi  R. SvobodaCalDavis  B.
SzczerbinskaTexasAMcorpuscristi  A. M. SzelcEdinburgh  H. TanakaSLAC  S.
TangBrookhaven  A. TapiaMedellin  B. Tapia OreguiTexasaustin  A.
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ZuklinCzechAcademyofSciences  V. ZutshiNorthernillinois  R. ZwaskaFermi

(August 27, 2024)

###### Abstract

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies,
neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is
a 6 $\times$ 6 $\times$ 6 m3 liquid argon time-projection-chamber (LArTPC)
that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a
prototype of the DUNE Far Detector. Charged particles propagating through the
LArTPC produce ionization and scintillation light. The scintillation light
signal in these detectors can provide the trigger for non-beam events. In
addition, it adds precise timing capabilities and improves the calorimetry
measurements. In ProtoDUNE-DP, scintillation and electroluminescence light
produced by cosmic muons in the LArTPC is collected by photomultiplier tubes
placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP
photon detection system performance is evaluated with a particular focus on
the different wavelength shifters, such as PEN and TPB, and the use of Xe-
doped LAr, considering its future use in giant LArTPCs. The scintillation
light production and propagation processes are analyzed and a comparison of
simulation to data is performed, improving understanding of the liquid argon
properties.

###### Keywords:

Noble liquid detector photon detector photomultiplier neutrino detector liquid
argon scintillation light dual phase TPC PEN TPB Xe doping

††journal: Eur. Phys. J. C

##  1 Introduction

The Deep Underground Neutrino Experiment (DUNE) DUNEtdrv1  aims to address key
questions in neutrino physics such as measuring the CP violating phase and the
neutrino mass ordering with an intense muon neutrino beam produced at Fermilab
DUNE_LBL . The physics program also addresses non-beam physics such as nucleon
decay and beyond the Standard Model searches DUNE_BSM  and the detection and
measurement of the electron neutrino flux from a potential core-collapse
supernova within our galaxy DUNE_SNB . DUNE will consist of a near detector
placed at Fermilab close to the production point of the neutrino beam of the
Long-Baseline Neutrino Facility (LBNF) to measure the unoscillated neutrino
interaction rate, and four 17-kt liquid-argon time-projection chambers
(LArTPCs) as far detector in the Sanford Underground Research Facility (SURF)
at 4300 m.w.e. depth at 1300 km from Fermilab DUNEtdrv4 ; duneIDRv3  where the
neutrino interaction will be measured after neutrinos have oscillated.

Figure 1: Views of ProtoDUNE-DP. Dimensions and positions of the major
elements are indicated (in units of centimeters). The drift direction
corresponds to the $y$-axis. The PMTs are represented with circles and, in the
top view, the empty circles correspond to PEN PMTs and the filled circles to
TPB PMTs.

The ProtoDUNE Dual Phase (DP) detector wa105 ; Cuesta:2019yeh  was operated
from 2019 to 2020 at the CERN Neutrino Platform to demonstrate the LArTPC DP
technology at large scale as a prototype for one of the DUNE far detector
modules. ProtoDUNE-DP has an active volume of 6$\times$6$\times$6 m3
corresponding to an active mass of 300 t (total LAr mass of 750 t), being the
largest DP LArTPC ever operated. In ProtoDUNE-DP the electric drift field is
oriented in the vertical direction, causing the electrons to drift vertically
towards the anode at the top. The ionization charge is then extracted,
amplified, and detected in gaseous argon above the liquid surface by the
charge readout planes (CRPs). The DP technology allows a good signal to noise
ratio ($>$10 for 6-m drift at 500 V/cm drift field) and a fine spatial
resolution ($\sim 3.125\times 3.125\times 0.64\,mm^{3}$) Aimard_2021  enabling
the construction of large active volumes making efficient use of the LAr
volume. The CRP consists of the extraction grid which bounds the active
volume, large electron multipliers (LEMs) to produce the charge avalanche, and
the anode to collect the electrons. In ProtoDUNE-DP, two fully instrumented
CRPs of 3 $\times$ 3 m2, and two CRPs without LEMs are installed. The
scintillation light signal is collected by a photon detection system (PDS)
constructed out of photo-multiplier tubes (PMTs). The PDS goals are to provide
a trigger and to determine precisely the event time, with capability to
perform calorimetric measurements and particle identification. Two Cosmic Ray
Tagger (CRT) panels with eight scintillator bars (1.44 m $\times$ 0.12 m) per
panel are placed on opposite sides of the ProtoDUNE-DP cryostat to trigger on
muon-tracks passing through both CRTs. Fig. 1 shows a diagram of ProtoDUNE-DP
with the layout and dimensions of the CRPs, PMTs and CRTs. The PDS layout is
optimized to maximize the collected light anne_phd .

As charged particles pass through LAr, they create pairs of positively charged
argon ions (Ar+) and free electrons and also produce excited atoms (Ar∗). When
ionized and excited argon atoms couple to neutral Ar atoms, they produce the
molecular states Ar${}^{+}_{2}$ and Ar${}^{\ast}_{2}$, respectively. The first
one eventually recombines with an electron producing Ar${}^{\ast}_{2}$. In
both processes (recombination and excitation) the decay of the final state
results in the emission of a vacuum ultraviolet (VUV) photon within a
wavelength centered at $127\pm 8$ nm Heindl:2010zz  constituting the primary
scintillation light (S1) signal:

$\displaystyle\mathrm{Rec.}:$
$\displaystyle\;\mathrm{Ar}^{+}+\mathrm{Ar}\rightarrow\mathrm{Ar}_{2}^{+}+\mathrm{e}^{-}\rightarrow\mathrm{Ar}_{2}^{*}\rightarrow
2\mathrm{Ar}+\gamma,$ (1) $\displaystyle\mathrm{Exc.}:$
$\displaystyle\;\mathrm{Ar}^{*}+\mathrm{Ar}\rightarrow\mathrm{Ar}_{2}^{*}\rightarrow
2\mathrm{Ar}+\gamma.$ (2)

In order for the recombination process to occur, an electron cloud surrounding
the Ar${}^{+}_{2}$ is needed. Hence the scintillation light yield depends on
the electric field. Ar${}^{\ast}_{2}$ has two possible states, a singlet and a
triplet state. The singlet transition to the ground state Ar2 has a short
decay time $\tau_{\mathrm{fast}}\sim$6 ns, while the triplet transition to the
same ground state is allowed only because of spin-orbit coupling and has a
much longer lifetime $\tau_{\mathrm{slow}}\sim$1.6 $\mu$s Hitachi .

In addition, electroluminescence secondary scintillation light, called S2, is
produced in the gas phase of the dual phase LArTPC when electrons, extracted
from the liquid, are accelerated in the electric field of the LEMs. The S2
signal also has 127 nm wavelength. The time difference between the S1 and the
S2 signals reflects the drift time of the original ionization in the liquid
phase up to the gas phase, and the S2 duration, which can be up to hundreds of
microseconds, is directly related to the track topology and covers the span of
the electron drift time.

The PDS of ProtoDUNE-DP protoDUNElight  is formed of 36 8-inch cryogenic
R5912-02MOD PMTs from Hamamatsu protoDUNEPMTs ; Belver:2020qmf , placed below
the cathode grid. As the PMTs are not sensitive to VUV light, a wavelength
shifter converts 127-nm photons into visible photons. Two different wavelength
shifters were deployed. A sheet of polyethylene naphthalate (PEN) is placed on
top of 30 PMTs and the other 6 PMTs have tetraphenyl butadiene (TPB) directly
coated on them. Fig. 2 shows a picture of the PDS installed in ProtoDUNE-DP.

Figure 2: ProtoDUNE-DP PDS formed of 36 PMTs installed at the bottom of the
cryostat at CERN. The front left PMT has TPB coating and the front right PMT
has PEN sheet.

A light calibration system (LCS) was developed for ProtoDUNE-DP to monitor the
PMT performance and obtain an equalized PMT response Belver:2019lqm . The
light source consists of 6 blue LEDs and a silicon photo-multiplier (SiPM) as
reference sensor. The LEDs use a Kapustinsky 1985NIMPA.241..612K  circuit as
LED driver, and have a wavelength of 465 nm that matches the PMT maximum
quantum efficiency. The calibration light is transmitted through a fiber
system with a fiber-end pointing at each PMT photocathode.

This paper describes the ProtoDUNE-DP PDS performance after 18 months of
operation considering its future use in giant LArTPCs with a particular focus
on the wavelength shifting techniques. A study of the LAr scintillation and
electroluminescence light production and propagation over large distances is
presented. In section 2, the ProtoDUNE-DP operation and collected light data
are described. Section 3 details the performance of ProtoDUNE-DP PDS. In
section 4 the light simulation tools are detailed. The analyses of
scintillation light propagation and production are discussed in section 5 and
the cosmic muon rate measured in ProtoDUNE-DP is described in section 6.
Studies using the electroluminescence light signal are summarized in section
7. Finally the performance of the system using Xe-doped LAr is reviewed in
section 8.

##  2 ProtoDUNE-DP PDS operation at CERN

ProtoDUNE-DP collected cosmic-ray data from June 2019 until November 2020,
operating with pure LAr and Xe-doped LAr in different conditions of electric
fields. Muons are the most abundant charged particles in cosmic rays at
surface, together with protons ($\sim$1%), and electrons and positrons
($\sim$0.1%). No beam data were taken with ProtoDUNE-DP.

The ProtoDUNE-DP operation faced issues that impacted the electric field
conditions. A short circuit between the high-voltage (HV) source and a field-
cage ring at approximately 1/3 the field-cage depth prevented reaching the
nominal cathode voltage (-300 kV). The cathode HV was set at -50 kV and the
drift field in the operation conditions was fairly uniform in the top part of
the drift and away from the field cage walls. A COMSOL COMSOL  simulation of
the resulting drift field is shown in Fig. 3. Additionally, CRP operation was
not straightforward due to the presence of bubbles on the LAr surface causing
LEM and grid discharges. Given these limitations, data taken with the CRPs in
combination with the PDS were very limited and the data presented in this
paper were taken with the PDS alone. The operation conditions of ProtoDUNE-DP
were 88 K with a thermal gradient present in the detector less than 20 mK and
1045 mbar.

Figure 3: Map of the drift field in ProtoDUNE-DP with cathode at -50 kV
simulated using COMSOL. A particular plane of the active volume is shown
(vertical plane at 130 cm from a field cage wall). The drift direction is
along the y-axis and the color scale represents the electric field strength.
The discontinuity at y$\sim$100 cm is due to the HV failure described in the
text.

The PDS started its operation as soon as the detector was purged with argon
gas in June 2019. ProtoDUNE-DP operated fully filled with LAr from August 2019
until May 2020. In June 2020, an intervention on the HV extender was carried
out with the aim of fixing the short circuit although the issue was not
solved. In July 2020, the detector was re-filled using $\sim$230 ton of Xe-
doped LAr from ProtoDUNE-SP protoDUNESP  contaminated with N2. In August 2020,
operations were resumed and two additional N2 injections took place to measure
the effect of N2 contamination in the light attenuation length. Table 1
summarizes the different doping concentrations in ProtoDUNE-DP.

Table 1: Summary of Xe and N2 doping concentrations in ProtoDUNE-DP. Units are ppm in mass for Xe, and ppm in volume for N2. Situation | [Xe] (ppmm) | [N2] (ppmv)
---|---|---
LAr + Xe + N2 | 5.8 | 2.4
1st N2 injection | 5.8 | 3.4
2nd N2 injection | 5.8 | 5.3

The PDS took data on a daily basis during short time periods (typically 1-2
hours/day). PMT HVs had to be switched off during the operation of some
monitoring systems that emit light, like the purity monitors or the cryogenic
cameras, to avoid any damage to the PMTs. Especially at the beginning of the
data taking period, this happened very often to survey the status of the
liquid surface and charge readout planes. Longer time periods of PDS data
taking ($\sim$12 hours) were allowed, typically at night, proving the
stability of the PDS.

Figure 4: Example of a PMT self-trigger event showing the waveforms
corresponding to the 36 PMTs according to their relative position in the
detector. The PEN PMTs are shown in black and TPB PMTs in red. The $x$-axis
range is 0-16 $\mu$s for all PMTs while the $y$-axis range (in ADC counts)
varies for each PMT and is optimized to best display the S1 signal.

A dedicated light data acquisition and calibration software was developed for
ProtoDUNE-DP ProtoDUNEDP_LACS . The software allows the user to choose the
acquisition trigger mode, control and define the acquisition settings (front-
end and high-voltage), and provides the graphical user interface. The light
readout front-end electronics is based on the commercial ADC V1740 from CAEN
CAEN . This 12-bit VME digitizer has 64 analog input channels with 2 Vpp
dynamic range and a maximum sampling rate of 62.5 MS/s. All data presented in
this paper were taken with 16-ns sampling, and different time acquisition
windows depending on the configuration (from 2 $\mu$s to 4 ms).

Individual PMT waveforms were recorded for analysis, so each event contains 36
waveforms. An example of a PMT self-trigger event is shown in Fig. 4 and an
individual PMT waveform is shown in Fig. 5. In the off-line analysis, various
properties of the waveform are evaluated: the integrated charge in a given
time period (in photo-electron units, PE), the time when the waveform reaches
the minimum value ($t_{0}$), the amplitude of the event, and the baseline mean
and standard deviation (STD).

Figure 5: Example of a scintillation light event in a ProtoDUNE-DP PMT at a
gain of $10^{7}$ in LAr without electric field.

The PDS system took data in three trigger modes:

  * •

PMT self-trigger: Provided by the PDS when a set number of PMT signals (or
more) over a given threshold are in coincidence. The trigger rate is in the
Hz-kHz range depending on the customizable threshold and the number of PMTs
required to pass the threshold. In this trigger mode, not only single tracks
are collected, but also showers and multi-track.

  * •

CRT trigger: In this case the PDS receives an external trigger signal from the
CRT planes at an average rate of 0.3 Hz. The CRT trigger mode allows the
recording of single tracks with known topology.

  * •

Calibration mode: An external trigger signal is received from the light
calibration system at 1 kHz synchronized with the calibration light pulse sent
to the PMTs. This mode also allows taking data with random trigger at a
configurable rate turning off the LEDs.

A total of 130.7 million events were acquired during a live time of 675 hours.
Table 2 shows the summary of the data taken for the different trigger
configurations. As shown in Table 3, most of the light data were acquired
without electric drift field, and therefore contain only primary scintillation
signals. Events with secondary light signals were collected for different
drift and amplification field conditions. An important sample of events was
recorded during the commissioning phase of the system with varying conditions
(test mode).

Table 2: Summary of trigger conditions indicating the number of events and total time of data taken in the different trigger configurations. Trigger | # of events (M) | Time (h)
---|---|---
PMT | 85.3 | 96
CRT Panels | 0.6 | 515
Calibration | 30 | 42
Random | 14.7 | 21
Total | 130.7 | 675
Table 3: Summary of the detector configurations indicating the number of events and total time of data taken with different voltage across LEMs. Drift field | LEMs voltage | # of events (M) | Time (h)
---|---|---|---
OFF | - | 85.1 | 342
ON | 0 kV | 13.6 | 72
| 2.5-3.6 kV | 7.7 | 212
Test | - | 23.2 | 48
Total | - | 130.7 | 675

##  3 ProtoDUNE-DP PDS performance

All 36 PMTs were operational throughout of data taking, allowing the
validation of the basic performance of the PDS. A coincident primary
scintillation light signal (S1) detected by all 36 PMTs is visible in Fig. 4.
The time alignment among PMT signals has been measured for all channels to be
better than 16 ns.

The low noise in the baseline of the signals is notable, as the baseline
presents very small fluctuation: $0.6\pm 0.1$ ADC ($0.29\pm 0.05$ mV). The
error includes the differences among PMTs and the stability along the time. In
addition, the baseline stability with time is shown in Fig. 6.

Figure 6: Baseline mean STD during ProtoDUNE-DP operation. A fit to the mean
value ($0.6\pm 0.1$ ADC) is shown in red and error bars represent the
differences among the different PMTs.

With the aim of understanding the performance of the PDS, the gain calibration
results are shown in section 3.1, the single photo-electron (SPE) is
characterized in terms of amplitude and rate in section 3.2, the relative
performance of PEN and TPB is evaluated in section 3.3, and the scintillation
time profile is studied in section 3.4.

###  3.1 Calibration

Figure 7: Gain ($G$) stability with time for the 36 PMTs (one color per PMT)
evaluated at 1500 V represented with respect to the average gain $<G>$.

The main goal of the LCS is to calibrate the PMT response by determining the
PMT gain during the operation of the detector. It is important to guarantee
equalized PMT response and to measure the light collected in PE units. An
accurate measurement of the collected light is essential for calorimetry and
to estimate the detection efficiency of the PDS. The PMTs are typically
operated at a gain in the range from $1\cdot 10^{7}$ to $5\cdot 10^{7}$ as a
compromise between maximizing the sensitivity to the SPE and minimizing ADC-
saturated events. The LCS illuminates the PMT photocathode at the SPE level in
order to determine the PMT gain. The calibration light rate is kept to
$\sim$kHz to avoid PMT fatigue Belver:2019lqm .

The gain calibration method, based on measuring the SPE charge at a given
voltage, is described in Belver:2019lqm . During operation, PMTs were biased
at the HV required to achieve the target gain according to the calibration
results. Calibrations were carried out weekly and a gain correction based on
the closest calibration in time is applied in the analysis.

The calibrations performed during the detector operation allow to monitor the
PMT gain stability with time. Fig. 7 presents the gain evolution of all the
PMTs during one year of regular operation of the LCS. It should be noted that
the PMTs were switched on and off every day (sometimes several times on the
same day). Despite this, it can be seen that the PMT gains are quite stable
with time. In particular, the average value of the gain STD at 1500 V for 36
PMTs is $9\%$.

###  3.2 Single photo-electron characterization

The capability of measuring low energy signals depends on the signal-to-noise
ratio and on the SPE rate from various background sources.

Figure 8: Mean SPE amplitude from Gaussian fits as a function of the PMT gain,
and linear fit. All PMTs are included but only data corresponding to gains
that allow a proper fit are considered. Figure 9: Example of SPE pulse in a
PMT waveform ($10^{7}$ gain).

The SPE amplitude is characterized as a function of the PMT gain using
calibration data. The SPE amplitude spectra are fitted with a Gaussian
function for each PMT in order to obtain the mean amplitude ($\mu$) and its
corresponding standard deviation ($\sigma$) as functions of the PMT gain. The
correlation between mean amplitude and gain is shown in Fig. 8, which allows
the average SPE amplitude at any gain to be extracted. For instance, at a gain
of $10^{7}$, the SPE amplitude is 7 ADC counts, implying a signal-to-noise
ratio greater than 11 thanks to the small fluctuation of the baseline in the
PMT waveforms, while $\sigma$ is 2 ADC counts. An example of a SPE pulse in a
PMT waveform at a gain of $10^{7}$ is shown in Fig. 9. The SPE pulses have a
full width at half maximum (FWHM) of 1 time sample (59%, 16 ns) or 2 time
samples (40%, 32 ns).

The SPE rate is computed by counting the SPE pulses in the data samples taken
with the random trigger. Pulses with an amplitude within the expected range
($\mu\pm 2\sigma$) are considered, ignoring the parts of the waveform affected
by S1 signals larger than 1 PE. Without drift field, a SPE rate of $\sim$350
kHz is obtained for TPB PMTs, whereas PEN PMTs are less sensitive and a rate
of $\sim$170 kHz is measured.

There are several light contributions at the SPE level to the low-energy
background detected by the PMTs. First, according to data analysis, cosmic
muons are expected to be detected as SPE at $\sim$35 kHz. Second, according to
a dedicated simulation, natural radioactivity, mainly from 1.01 Bq/kq 39Ar
Benetti:2006az , 115 mBq/kg 85Kr Benetti:2006az  and 0.09 mBq/kg 42Ar
Barabash:2016lru , is expected to contribute with $\sim$20 kHz. Lastly, it is
known that the PMT dark current contributes with a SPE rate of $\sim$1.7 kHz
protoDUNEPMTs . However, the measured rate is higher than the expectation from
these sources. A study Luo:2020itx  suggests that Ar${}_{2}^{+}$ drifting to
the cathode could explain the high SPE rate recently measured in several
experiments as these molecules recombine with electrons and neutralize with
electronegative impurities producing photons. Unfortunately, this hypothesis
cannot be tested in our detector because of the non-uniform drift field.

###  3.3 Wavelength-shifting materials: PEN and TPB

The LAr scintillation light is produced at 127 nm, a wavelength which most
photosensors are not sensitive to, and fluorescent materials are introduced to
shift the photon wavelength towards the visible range. ProtoDUNE-DP uses PMTs
either covered with polyethylene naphthalate (PEN) foils or directly coated
with tetraphenyl butadiene (TPB). While TPB is broadly used, PEN is a novel
material, never used before in such a large scale experiment and whose
efficiency is not well known. As TPB needs complex coating setups
Bonesini_2018 , the potential benefit of PEN comes from its simple handling,
because PEN foils are flexible plastic sheets easy to fabricate and install.
The PEN sample used in ProtoDUNE-DP is transparent and biaxially oriented,
manufactured by GoodFellow GoodFellow_PEN . It has been installed as round
disks of 240 mm diameter and 0.125 mm thickness placed over the top of the PMT
glass surface, as shown in Fig. 2. TPB was deposited over the PMT polished
surface, using a dedicated evaporation system developed by the ICARUS
experiment Bonesini_2018 . The coating density is 0.2 mg/cm2, which
corresponds to a coating thickness around 0.2 µm. Both PEN and TPB have a
maximum of re-emitted photons around a wavelength of 430 nm PEN-DMary ; TPB-
Francini2013 .

Photons produced after cosmic particles interact with the LAr will arrive at
the wavelength-shifter (either PEN or TPB) and will convert into visible light
that can be detected by the PMTs. The relative photon detection efficiency of
the PEN-foil PMTs versus the TPB-coated PMTs,
$NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$, is experimentally determined by
comparing the amount of light (in PEs) detected by a pair of PEN-TPB PMTs
placed symmetrically with respect to the detector and the light source. An
homogeneous response for the whole photocathode is considered.

Dedicated data-sets were taken selecting events with a signal amplitude larger
than 13 PEs in the trigger PMT, a TPB-coated PMT placed at the center of the
detector (channel 16 in Fig. 1). The PMT pairs are selected among PMTs
symmetrically placed near the trigger PMT (for example, channels 17 and 23).
As the event position is not known, the number of photons reaching the PMTs
cannot be compared on an event by event basis, but it can be assumed that the
amount of photons reaching both PMTs is on average the same. The cosmic-muon
flux is assumed to be isotropic.

The response of five PMT pairs is compared at different gains (107,
2$\cdot$107, 5$\cdot$107 and 108). The trigger configuration was kept constant
in all data-sets, with the same gain (5$\cdot$106) and threshold (13 PEs in
amplitude) in the trigger PMT in order to study the same event sample at
different gains. To avoid saturation of the ADC and guarantee linear response
of the PMTs, events with a signal amplitude larger than $\sim$30 PEs on the
trigger PMT are not included. This selection reduces the fraction of
saturating events below 1% in all PMTs in all data-sets.

The average light collected on the PMTs for the selected events is $\sim$200
PEs on TPB-coated PMTs, and $\sim$50 PEs on PEN-foil PMTs. The
$NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$ ratio is stable for each pair at
different gains and different ranges. An average
$NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$ ratio of $0.25\pm 0.03$ is obtained,
as reported in Table 4. The error is the STD among PMT-pairs, which agrees
with the expected error (0.03) due to the QE variation between 3 PMTs measured
by the manufacturer. On average, ProtoDUNE-DP TPB-coated PMTs detect four
times more photons than PEN-foil PMTs. This ratio is computed for a particular
sample of muons; selecting events with a different track topology would vary
this ratio.

Table 4: First row: Value of the relative photon detection efficiency, $NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$. The error represents the STD for all the PMT pairs. Second row: relative WLS efficiency of PEN and TPB, $\epsilon_{\mathrm{PEN}}/\epsilon_{\mathrm{TPB}}$. Parameter | Value
---|---
$NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$ | $0.25\pm\,0.03$
$\epsilon_{\mathrm{PEN}}/\epsilon_{\mathrm{TPB}}$ | $0.35\pm 0.09$

Additionally, a simple model is proposed to compute the relative WLS
efficiency of the two materials. The number of detected photoelectrons is
given by:

$\centering NPE=\gamma\cdot\epsilon\cdot\Delta\cdot QE,\@add@centering$ (3)

where $\gamma$ is the number of VUV photons arriving to the wavelength
shifter, $\epsilon$ is the WLS efficiency, $\Delta$ is the photon transport
losses from the WLS to the PMT, and $QE$ is the PMT quantum efficiency. Then,
the relative conversion efficiency can be derived:

$\frac{\epsilon_{\mathrm{PEN}}}{\epsilon_{\mathrm{TPB}}}=\frac{NPE_{\mathrm{PEN}}}{NPE_{\mathrm{TPB}}}\cdot\frac{\gamma_{\mathrm{coat}}}{\gamma_{\mathrm{foil}}}\cdot\frac{\Delta_{\mathrm{coat}}}{\Delta_{\mathrm{foil}}}.$
(4)

The ratio of photons arriving to the TPB coating over the PEN foil is, on
average, $\gamma_{\mathrm{coat}}/\gamma_{\mathrm{foil}}=0.69\pm 0.16$, as
calculated using the cosmic-muon simulation described in section 4. This
factor depends on the selected sample, and the simulation of a different event
topology would vary its value. The fact that the PEN foil receives 30% more
photons than the TPB coating can be explained as its two faces are exposed to
LAr while the TPB only has one.

Considering an isotropic re-emission of the TPB coating, only 50% of the
photons will reach the photocathode ($\Delta_{\mathrm{coat}}$ = 0.5). For the
PEN foil, the transport losses are simulated considering an isotropic re-
emission in the foil, and it is found that $\Delta_{\mathrm{foil}}$ = 0.247,
meaning that only 25% of the re-emitted photons will reach the photocathode
due to the geometrical configuration of the foil with respect to the
photocathode.

As a result, considering the geometrical differences and the measured ratio
$NPE_{\mathrm{PEN}}/NPE_{\mathrm{TPB}}$, the relative WLS efficiency of both
materials is estimated to be
$\epsilon_{\mathrm{PEN}}/\epsilon_{\mathrm{TPB}}=0.35\pm 0.09$, as shown in
Table 4. TPB produces three times more visible photons than PEN, for the same
amount of incident VUV photons. This agrees with the value of $0.34\pm 0.01$
reported in PEN-Texas  for the same PEN sample.

To introduce the PMT response in the simulation, the effective PMT photon-
detection efficiencies at 127 nm for TPB-coated PMTs ($E_{\mathrm{TPB}}$) and
for PEN-foil PMTs ($E_{\mathrm{PEN}}$) are estimated. This effective
efficiency provides the amount of photoelectrons detected per incident VUV
photon, and it can be calculated by:

$\centering E=\epsilon\cdot\Delta\cdot\text{QE}\,.\@add@centering$ (5)

The PMT quantum efficiency (QE) was measured by the manufacturer for three of
the PMTs at room temperature. They measured a value of QE = $0.183\pm 0.013$
at 430 nm, and it is assumed that the PMT QE is stable when going to cryogenic
temperature Bueno_2008 ; Zhao_2021_PMT_QE_CT . Since the values of TPB
efficiency at 127 nm reported in the literature show a large dispersion TPB-
Benson2018 ; TPB_Graybill ; Lally_TPB , a 100% TPB efficiency is assumed as in
a previous work 311light  ($\epsilon_{\mathrm{TPB}}$=1). Then, assuming the
PEN efficiency obtained by the relative performance of both systems in Table 4
($\epsilon_{\mathrm{PEN}}=0.35\pm 0.09$), the corresponding effective
efficiency for TPB-coated and PEN-foil PMTs are $E_{\mathrm{TPB}}=0.09$ and
$E_{\mathrm{PEN}}=0.016$, making the TPB coated-PMT six times more efficient.
However, positioning the PEN foil directly over the PMT glass, as the TPB
coating, would double its effective efficiency. These efficiencies are used in
the analyses presented in sections 5 and 6.

###  3.4 LAr scintillation time profile

The scintillation light emission in LAr has a characteristic time dependence
as mentioned in section 1. To get the scintillation decay times from the PMT
waveforms, signals from cosmic muons are selected by triggering on a TPB-
coated PMT with a minimum amplitude of 25 PEs. The PMT gain is set at
5$\cdot$106 to minimize the ADC saturation, and events saturating the PMT
Lastoria:2748990  are excluded. Eleven of the PMTs suffer this phenomenon more
frequently and are excluded for this analysis.

An average time profile is generated for each PMT. The average waveform in the
absence of drift field for one PMT is shown in Fig. 10. Waveforms are well
described by Eq. 6:

$f(t)=\sum_{j=\text{fast,slow,int}}\frac{2A_{j}}{\tau_{j}}\exp\left[\frac{\sigma^{2}}{2\tau_{j}^{2}}-\frac{t-t_{0}}{\tau_{j}}\right]\times$
$\times\left(1-\text{Erf}\left[\frac{\sigma^{2}-\tau_{j}(t-t_{0})}{\sqrt{2}\sigma\tau_{j}}\right]\right)$
(6)

a sum of three exponential functions convoluted with a Gaussian function to
represent the detector response. Although the scintillation time profile
should in principle have only two components, from the decay to ground state
of singlet ($\tau_{\mathrm{fast}}$) and triplet ($\tau_{\mathrm{slow}}$) argon
excimers, an intermediate component ($\tau_{\mathrm{int}}$) is added in order
to improve the fit as reported also by other LAr experiments 311light . Given
the 16-ns digitization sampling, the fit has a limited sensitivity to
$\tau_{\mathrm{fast}}$ and this parameter is fixed to 6 ns Hitachi .
Additionally, two signal reflections at the flange feed-through appear
$\sim$200 ns and $\sim$400 ns after the maximum, affecting the sensitivity to
the $\tau_{\mathrm{int}}$ measurement. To mitigate this, the bins containing
the reflections are excluded from the fit.

Figure 10: Example of the average scintillation waveform of a PEN PMT (blue).
The fit to Eq. 6 is shown in red. The corresponding parameters are shown in
the legend.

Purity is critical in LArTPCs, since impurities can reduce the signal by
trapping the ionization electrons. The purity can be monitored using the PDS
by measuring the lifetime of the triplet molecular argon excimers,
$\tau_{\mathrm{slow}}$. Fig. 11 shows the evolution of the average
$\tau_{\mathrm{slow}}$ during the operation of ProtoDUNE-DP. The purity
improved when the LAr purification system was turned on, and remained stable
during the whole operation. The value of $\tau_{\mathrm{slow}}$ is $1.46\pm
0.02$ $\mu$s, with the error corresponding to the STD among the PMT waveforms.
This average value has a small variation over time of just $0.004$ $\mu$s. The
absolute value indicates a high LAr purity at the ppb level. No significant
difference is observed in $\tau_{\mathrm{slow}}$ between PEN and TPB PMTs. On
average, $\tau_{\mathrm{slow}}$ is $1.45\pm 0.02\,\mu$s on PEN PMTs and
$1.46\pm 0.02\,\mu$s on TPB PMTs.

Figure 11: Monitoring of the $\tau_{\mathrm{slow}}$ component of the
scintillation time profile during the detector operation. The error bars show
the STD of the measurements for the different PMT waveforms. Red line at
$\tau_{\mathrm{slow}}=1.46\leavevmode\nobreak\ \mu$s shows the average from
September 2019 when stable operation began.

Fig. 12 shows the value of $\tau_{\mathrm{int}}$ for PEN and TPB PMTs obtained
from the fit of the average waveforms using data sets for which the purity was
already stable, from September 2019 to May 2020. An average value of $50.3\pm
1.7$ ns is obtained for the PEN PMTs, and a faster response of $43.6\pm 0.7$
ns for the TPB PMTs. The clear difference between the two different WLS points
to a delayed emission time by the WLS material, as proposed in Segreto ;
Whittington .

Figure 12: $\tau_{\mathrm{int}}$ distribution obtained from the fit to the
average waveform for PEN (blue) and TPB PMTs (red).

##  4 Cosmic-muon light simulation

ProtoDUNE-DP light simulations are carried out using LArSoft LArSoft , a
physics software package designed for LAr neutrino experiments, such as DUNE.

The detailed geometry of the ProtoDUNE-DP detector has been implemented in
Geant4 geant4  using geometry description markup language (GDML) files. It
includes the full cryostat with its support structure, cathode, field cage,
LEMs, PMTs and ground grid.

The simulation of the cosmic-induced particles reaching ProtoDUNE-DP is based
on CORSIKA (COsmic Ray SImulation for KAscade) CORSIKA , a Monte-Carlo (MC)
simulation package. It is based on a multi-component model of primary cosmic
rays, the constant mass composition (CMC) model forti , for a more complete
modeling of the flux. CORSIKA generates showers from each specific cosmic ray
type (Fe, He2, Mg, proton) according to a power law distribution of the
primary particle energy. The FLUKA model FLUKA  describes the hadronic shower
propagation. The LArSoft simulation draws from a random set of pre-generated
air showers from a specific database created with CORSIKA. The number of
showers per event and per primary particle source is extracted according to a
Poisson distribution around the predicted average number of primary cosmic
rays for each source. The detector location (CERN), altitude and latitude, is
used. The start time and spatial origin of the cascades of secondary particles
are chosen randomly, and time and space correlations of the particles within
each shower are preserved.

Muons crossing the CRT panels of ProtoDUNE-DP are also simulated. Single muons
are generated with a defined initial position, momentum, and momentum spread.
The energy distribution of the muons is taken from CORSIKA for a more
realistic outcome. The data-driven track entry/exit distributions in the CRT
panels are taken as inputs to define the track topology of the events. A
topological selection is carried out and only muons with a complete trajectory
between the CRTs are accepted, as deviations from the ideal straight
trajectory can appear during the propagation through the detector and muons
can decay along the track length.

The cosmic muons simulated with the event generators enter the LAr volume and
deposit energy along their tracks as they interact with LAr. An incident muon
behaves as a minimum ionizing particle (MIP) and deposits about 2 MeV/cm.
First, the simulation of the muon energy deposition is performed in Geant4.
Then, the number of scintillation photons is computed by multiplying the
deposited energy by a light yield of 4$\cdot 10^{4}$ scintillation photons per
MeV in the absence of a drift field. The time profile is simulated with an
exponential fast decay of 6 ns for 30% of the photons, and an exponential slow
decay of 1590 ns for the other 70% of the photons. Simulations were carried
out before obtaining the ProtoDUNE-DP results described in section 3 and no
intermediate component is included. No drift field is assumed in this
simulation to match the data taking conditions. Note also that only cosmic
particles are simulated (only muons in the CRT case), and backgrounds from
natural radioactivity are not currently included in the simulation.

The photon propagation in LAr, from the production point to the PMT array, is
performed with Geant4 in LArSoft. The VUV-light attenuation due to absorption
by impurities in LAr is simulated (20-m absorption length, equivalent to 3
ppmv of nitrogen contamination in LAr Jones:2013bca ) as well as the Rayleigh
scattering length (RSL) (99.9 cm as baseline value Babicz:2020den , but also a
shorter length of 61.0 cm GRACE2017204  is tested). A VUV reflectance of 26%
in aluminum is taken (field cage) ICARUStechnote , and the same VUV
reflectance is assumed for stainless steel (SS) surfaces (cryostat walls,
cathode and ground grid) Zatschler:2020yjp . Full absorption is considered for
the rest of the materials. Table 5 summarizes the relevant parameters of the
light propagation used in the simulation.

Table 5: Parameters of the light propagation simulation in ProtoDUNE-DP. Parameter | Baseline value | Alternative
---|---|---
Absorption length | 20 m | -
RSL | 99.9 cm | 61.0 cm
VUV Reflectance | 26% in Al & SS, | 0% for all
| 0% for the rest |
Voxel size | 0.34 $\times$ 0.32 $\times$ 0.34 m3 | -
Figure 13: Maps of the detected light (sum of PMT visibilities multiplied by
efficiency, $E_{\mathrm{TPB}}=0.09$ and $E_{\mathrm{PEN}}=0.016$) per voxel
for the baseline photon library in ProtoDUNE-DP: 4$\cdot 10^{4}$ photons per
MeV, 99.9 cm of RSL and 26% VUV reflections. The PMT positions are represented
with black dots and the active volume with a red rectangle.

The complete simulation of the propagation of all the photons produced by each
particle crossing the detector would require an enormous amount of CPU time.
Hence, pre-generated libraries are employed to simulate the light propagation
in an efficient way: a Geant4-driven simulation is generated and the results
are stored and parametrized in the so-called photon libraries. The LAr volume
is divided into 3D voxels and 108 photons per voxel are isotropically and
uniformly generated. A photon library contains, for each PMT-voxel
combination: the visibility (photon detection probability), the minimum time
for the light to reach the PMT (arrival time), and the Landau-fit parameters
of the propagation time distribution. In order to mitigate the relatively
large size of the voxels, 3D interpolations among them are used at each step
of the track.

Three different photon libraries are generated to study the impact of several
parameters in ProtoDUNE-DP: (A) the baseline photon library with 99.9-cm RSL
and 26% VUV-light surface reflectance, (B) a library with 61-cm RSL and 26% of
surface reflectance, and (C) a library with 99.9-cm RSL and no reflection. The
maps with the detected light per deposited MeV across the volume for the
baseline photon library (A) are displayed in Fig. 13.

The PMT response is simulated with a dedicated module that produces a waveform
for each PMT. Each 127-nm photon arriving to the WLS has a probability to
produce a photoelectron in the PMT photocathode. The WLS-PMT photon-detection
efficiency includes the WLS efficiency, the propagation of the visible light
from the WLS towards the photocathode, and the PMT quantum efficiency. The
values used in the light simulation are taken from studies presented in
section 3.3: $E_{\mathrm{TPB}}=0.09$ and $E_{\mathrm{PEN}}=0.016$.

Waveforms are produced by adding the SPE response for each detected photon.
The simulated PMT response is linear, and includes a dark current component of
1.7 kHz Belver:2019lqm ). The waveform digitization considers a 16-ns sampling
matching the data acquisition system.

##  5 Light production and propagation in LAr measured with cosmic muon data

The pure LAr of ProtoDUNE-DP is an optimal medium to study the scintillation
light production, propagation and collection in a LAr volume. The experiment
provides valuable technical feedback for future light detection systems as
well as results on the scintillation light mechanisms in LAr. In this section,
PMT data acquired with the CRT-trigger system are analyzed to profit from the
off-line reconstruction of the track trajectory that such a trigger allows.
Muon tracks crossing the detector diagonally and in the downward direction are
selected. Information about the distance the light travels from its production
point in the LAr volume to the PMT detecting the signal is retrieved.

The analysis focuses on the 30 PEN PMTs because they allow exploring a wider
track-PMT distance range than the TPB PMTs. Consistent results are obtained
with the TPB PMTs. The S1 charge (number of PEs) of each triggered signal is
obtained by integrating the PMT waveform in a 4-$\mu$s window. An event
selection is made in both data and simulation to obtain comparable event
samples:

  1. 1.

Time of flight (data) / geometrical cut (MC): a data event is considered a
good muon-candidate if the time of flight between the CRT panels (from top to
bottom) is between 40 and 45 ns, and only one scintillating bar per panel is
triggered. This cut contributes to the rejection of background events like
electrons, particle showers, muon bundles and fake triggers. In the simulation
case, only CRT-trigger-like muons are kept by requiring that the muon-track
trajectory crosses both CRTs.

  2. 2.

Stable baseline before trigger: this cut intends to avoid the pile-up of
signals. The waveform RMS is obtained in a 1-$\mu$s window before the signal
trigger and the baseline is considered to be stable if the result is below 1
ADC count.

  3. 3.

No ADC saturation: data events with waveforms saturating the ADC are excluded,
both in data and MC.

  4. 4.

Maximum charge: the charge per event must be below 100 PE for PEN PMTs. This
limit is applied to suppress high-energy events (vertical showers instead of
diagonal muons) that affect the distribution of interest in data but not in
the MC as they are not simulated, see Fig. 14.

Figure 14: Normalized distributions of the collected S1 charge for CRT
triggered light data and simulation.

Figure 15: Collected S1 charge versus track-PMT distance for PEN PMTs: Light
data (top) and MC sample (bottom). The color maps contain all the S1 signals
detected by the PMTs and passing the event selection. A Gaussian fit of each
charge-distribution every 10 cm is performed and the mean values are plotted
in black over the map. The vertical error bars correspond to errors from the
fits and a 5-cm distance uncertainty (horizontal error bars) is included.

Since the propagation effects on the collected charge by the PMTs depend on
their distance to the muon track, analyses shown in this section are mainly
based on the correlation between the S1 signal (S1 charge in PE units) and the
minimum distance from the muon track to the detecting PMT (simply referred to
as track-PMT distance). Fig. 15 shows the 2D distribution of theses variables
for data and MC. A good agreement is observed up to $\sim$5 m; beyond that
distance, the correlation between charge and distance is lost in data,
presumably, because low-energy background (only present in data and
contributing with up to 8-9 PE per integration window) prevails over the
signal. The profile histogram superimposed on the 2D plot corresponds to a
Gaussian fit of the charge-distribution every 10 cm. In the case of the data
samples, an additional systematic error of 4% (determined by varying the PMT
gain) is added in quadrature to the fit error of the charge.

Figure 16: Average S1 charge per channel (in PE) in CRT-trigger mode: top view
of PEN-PMT array for data (top panel) and simulation (bottom panel). Each
color square represents one PMT channel at its position in the detector. The
approximate positions of the CRT frames are indicated with black lines.

The top-view light detection maps (average S1 charge per optical channel) for
the PEN PMTs both for data and MC are presented in Fig. 16. The obtained light
detection patterns reproduce the expected gradient: the PMTs which are closer
to the bottom CRT (top right corner in the top view) detect more light than
the rest.

###  5.1 Drift field effect on light production

The reduction of the detected light with the increasing drift field is
investigated. This reduction is due to the suppression of the electron-ion
recombination by drift field, which reduces the primary scintillation light
production. The electric field in ProtoDUNE-DP is not uniform across the
active volume, which makes infeasible a complete understanding of the drift
field effect over the light production. Nonetheless, the light levels detected
with the PDS without drift field and at the maximum operating cathode HV (-50
kV) are compared to roughly quantify the light yield decrease. Fig. 17 shows
the charge-distance distributions at the two cathode HVs and the corresponding
ratios between them. The ratio as a function of the track-PMT distance is
fitted to a constant to obtain the average ratio of 0.833 $\pm$ 0.007, which
means that at least 17% of the scintillation light detected in the absence of
a drift field comes from electron-ion recombination.

Figure 17: Top panel: Average S1 charge collected by the PEN PMTs as a
function of the track-PMT distance without drift field (black) and with
cathode at -50 kV (red). The larger error bars at short distance are due to a
lack of statistics. Bottom panel: Ratio between the two distributions. The
ratio is fitted to a constant value (result: 0.833 $\pm$ 0.007).

The CRT-trigger muons cross different fields, which leads to a difficult
quantification of the overall drift field responsible for the light level
reduction. An estimate of the effective electric field along the CRT-trigger
muon tracks was made using the 3D simulation of the drift field in ProtoDUNE-
DP corresponding to a cathode HV of -50 kV, see Fig. 3. The average field
value obtained for these tracks is 0.09${}^{+0.10}_{-0.02}$ kV/cm, where the
errors are determined asymmetrically, as the RMS of the values above and below
the mean value separately. The reduction of the S1 signal for this drift field
is plotted in Fig. 18 and follows the empirical Birks’ law. Despite the
relatively large uncertainty of the ProtoDUNE-DP result, a fair agreement is
found with the literature for both ground-level cosmic muons 311light  and
MeV-electrons Kubota ; Aris .

Figure 18: Light yield reduction with drift field measured by various
experiments (red triangles from Kubota , black squares from Aris , blue
inverted triangles from 311light ), and the ProtoDUNE-DP result (orange
circles) discussed in this section.

The scintillation time profile is also dependent on the drift field. The two
excited molecular states, singlet and triplet, are formed either through
recombination or excitation with different probability, in such a way that the
normalization constants, $A_{\mathrm{fast}}$ and $A_{\mathrm{slow}}$ of Eq. 6,
are expected to change as a function of the drift field as the light emitted
by electron-ion recombination is suppressed. According to Kubota  the ratio
$(A_{\mathrm{fast}}+A_{\mathrm{int}})/A_{\mathrm{slow}}$ is expected to
decrease but the recent measurement presented in 311light  shows an increase
of 34% at 500 V/cm. ProtoDUNE-DP has observed an increase of 28% at -50 kV
cathode voltage, corresponding to an average field value of
0.09${}^{+0.10}_{-0.02}$ kV/cm, which is consistent with the 23% measured in
311light  at a similar electric field strength.

A decrease of $\tau_{\mathrm{slow}}$ with the drift field was reported for the
first time in 311light  and is also observed in ProtoDUNE-DP, see Fig. 19. A
model is proposed in Segreto:2020qks , taking into account the quenching of
the long-lived triplet states through the self-interaction with other triplet
states or through the interaction with molecular Ar${}^{+}_{2}$ ions. It
successfully explains the experimentally observed dependence of
$\tau_{\mathrm{slow}}$ with the intensity of the applied electric field.

Figure 19: Evolution of $\tau_{\mathrm{slow}}$ with the cathode voltage for
CRT-trigger events. The results are obtained averaging PEN PMTs and the error
bars indicate the STD among them.

###  5.2 Light propagation

The size of ProtoDUNE-DP, the longest drift-distance LArTPC ever operated,
allows for an unprecedented study of the light propagation. The Rayleigh
scattering length (RSL) can impact on the amount of light collected. An
evaluation of the RSL value is carried out by comparing the measured light
signals with the light predicted by the MC simulation testing two lengths
(61.0 cm GRACE2017204  and 99.9 cm Babicz:2020den ) obtained in experimental
measurements. Modeling the dependence of the light attenuation from the track-
PMT distance with a decaying exponential function allows the measurement of
the overall effective attenuation in data and MC and the evaluation of the
agreement for the different simulated configurations. The track-PMT distance
range studied is 4-5 m to focus on the longer distances where the sensitivity
to the RSL effect is higher.

In Fig. 20, each S1 charge-distance correlation under study is fitted to an
exponential and the data-MC ratios for the two simulations are also presented.
Looking at the distribution shape, the agreement between data and the 99.9-cm
MC sample is better than with the 61.0-cm value. The attenuation length values
obtained are presented in Table 6, and it is observed that the data value also
agrees better with the 99.9-cm MC value. The attenuation length accounts for
the effective attenuation of the light in ProtoDUNE-DP, but it is not a
physical property of the LAr as it depends, among other factors, on the
topology of the selected tracks and detector geometry. The measured
attenuation length is higher than the RSL, so the light is expected to undergo
Rayleigh scattering before being heavily attenuated due to, for example,
absorption by LAr impurities or detector elements. This long light path before
absorption is achieved in ProtoDUNE-DP thanks to the excellent LAr purity and
the large free LAr volume with no nearby components.

Figure 20: Top panel: Average S1 charge collected by the PEN PMTs as a function of the track-PMT distance. Two MC samples with different RSL values (61.0 cm and 99.9 cm) are compared to data. For each distribution, an exponential fit is plotted as a solid line. Bottom panel: Data-MC ratio for the two previous MC samples. Table 6: Attenuation length values obtained from the exponential fits shown in Fig. 20. Sample |  $\lambda_{\mathrm{att}}$ (cm)
---|---
Data | 180 $\pm$ 17
MC (RSL 99.9 cm) | 180 $\pm$ 10
MC (RSL 61.0 cm) | 157 $\pm$ 8

By comparing MC samples generated with the photon libraries (A) and (C), the
impact of the VUV-photon reflections on the light reaching the PMT array can
be evaluated. It is found that 11% of the light detected by PEN PMTs in the
baseline MC corresponds to VUV light reflected on the field cage, cryostat
walls, cathode and ground grid.

Finally, the light detection is studied through the effective TPB and PEN PMT
efficiencies described in section 3.3. The data-MC charge ratios obtained are
0.898 $\pm$ 0.002 and 1.150 $\pm$ 0.005 for PEN and TPB PMTs, respectively, in
the selected track-PMT distance range. The result is that the simulation
efficiency is validated within 10-15% of data. The simulation underestimates
the light detected by the TPB PMTs, which may be because not all the reflected
light is simulated, or the TPB WLS efficiency may be under-estimated. However,
the light detected by PEN PMTs in data is lower than in the simulations, which
can be attributed to a PEN WLS efficiency over-estimation.

##  6 Measurement of the cosmic muon rate and light yield

The rate of cosmic particles crossing the detector is high because ProtoDUNE-
DP is located on the surface. The high rate represents a perfect test bench to
study the PDS capability for detecting muons and providing calorimetric
information.

The S1 rate and charge from cosmic muons crossing ProtoDUNE-DP as well as the
muon flux are evaluated in section 6.1. In addition, the observed light yield
from muons is investigated for two different WLS methods in section 6.2.

In total, five data sets acquired over seven months with the random trigger
are analyzed and compared with CORSIKA-based simulations. The cosmic-muon data
sample allows the validation of the PDS simulation to demonstrate that the
detector response is correctly modeled and understood. CRT-trigger data are
also analyzed to evaluate the detected light yield for different muon samples.

###  6.1 Measurement of the cosmic muon flux

The results shown in this section are based on the analysis of S1 signals from
light data acquired in random-trigger mode. These data comprise varied signals
as the light reaching the PMTs can be generated at any location within LAr
volume and be associated with very different track topologies and energies.

The muon S1 signals are identified in the PMT waveforms using a custom peak-
finding algorithm which takes the following aspects into account:

  1. 1.

Any pulse in the waveform with an amplitude larger than the expected SPE
amplitude plus 2$\sigma$ is considered a candidate S1 signal. The amplitude
threshold is based on the results of the average SPE amplitude presented in
section 3.2. The lack of $\tau_{\mathrm{int}}$ in the simulated scintillation
time profile (see section 4) results in a different charge-amplitude
correlation than in data and, hence, a correction of the amplitude is applied
in the MC analysis in order to select events that correspond to the same
deposited energy as in data.

  2. 2.

After a candidate S1 signal, a veto time window of 4.8 $\mu$s is applied. If
another candidate S1 signal is detected during that time, a new 4.8-$\mu$s
veto window beginning at that point is imposed.

  3. 3.

An optical reconstruction to reject small and uncorrelated signals is
performed by requiring coincidences among PMTs. A coincidence occurs when at
least two PMTs detect the candidate S1 signal within a time interval of 112
ns. This cut is effective in rejecting candidate S1 signals at low energy in
data, where the background, which is not considered in the MC, is dominant.
About 40% and 10% of candidate S1 signals are rejected in data and MC,
respectively.

The rate of S1 signals detected by each PMT is then computed. It should be
noted that the obtained S1 rate is not the overall cosmic muon rate in the
detector but the average muon rate per PMT. Results presented in Table 7 show
a higher rate of simulated S1 signals induced by muons: around 16% (11%)
higher S1 rates are detected by the TPB (PEN) PMTs in simulation than in data.
Nevertheless, the muon flux with the model chosen within CORSIKA to generate
the primary cosmic particles, the CMC model, is expected to be up to 20% above
the one obtained with a model assuming only cosmic protons, as reported in
microboone2021 , and the flux can be up to 25% lower with other particle
generators. Thus, the observed data-MC deviation is within the discrepancy
between the cosmic-ray generator models.

Table 7: Average S1 rate per PMT in random-trigger mode and data/MC ratio. The error of the S1 rate corresponds to the STD among PMTs. PMT | S1 rate per PMT (kHz) | Data/MC ratio
---|---|---
| Data | MC |
TPB | 8.8 $\pm$ 0.5 | 10.2 $\pm$ 0.5 | 0.86 $\pm$ 0.09
PEN | 5.5 $\pm$ 0.6 | 6.1 $\pm$ 0.4 | 0.90 $\pm$ 0.10

Finally, the atmospheric muon flux is assessed. Considering the surface
covered by the PMT array and the fraction of muons crossing such an effective
area in the MC, the predicted atmospheric muon flux at the Earth’s surface by
CORSIKA is 166 Hz/m2. Then, the predicted flux is scaled by the ratio between
the data and MC rates given in Table 7. A cosmic muon flux of
148${}^{+8}_{-11}$ Hz/m2 is obtained in ProtoDUNE-DP at CERN (at 455 m
altitude above mean sea level). The systematic uncertainty on the flux is
computed by varying the threshold in amplitude for the S1 identification by
$\pm$20% ($\sim$10-25 ADC) given the uncertainty in the MC waveform
simulation, and the PMT efficiency in the MC by $\pm$10% as concluded in
section 5. The ProtoDUNE-DP cosmic muon flux result is found to be consistent
with other measurements at ground-level given in the literature, as can be
seen in Fig. 21.

Figure 21: Cosmic muon flux determined in ProtoDUNE-DP at CERN at 455 m above
mean sea level (red square) compared to the measurements reported in
microboone2021  (filled black dots) and mitrica2011  (open black dot).

###  6.2 Observed light yield from cosmic muons

The light yield for muon interactions observed by the PDS is investigated
considering the use of the system as a calorimeter. The event selection of
random-trigger data explained in section 6.1 is applied. Good stability among
data taken throughout seven months in terms of S1 rate and charge collection
(2-5% of STD among different sets in both cases) is observed, which indicates
stable performance of the system.

Taking into account all the cosmic muon tracks across the LAr volume, the
average deposited energy per muon is determined with the CORSIKA cosmic-ray
simulation to be 813 MeV. Then, the observed light yield in random trigger
mode is calculated as the total S1 charge collected by all PMTs normalized by
the average deposited energy per muon. The results can be seen in Table 8.
This approach is validated by the fair agreement between the total S1 charge
collected by the PDS per muon in data (1830 PE) and MC (1990 PE), see Fig. 22.
In a similar way, the observed light yield is computed for S1 signals acquired
with the CRT trigger, see Table 8. In this case, based on the average track
length in LAr observed in data (9.4 m), an average deposited energy per muon
of 1880 MeV is obtained.

Table 8: Total S1 charge per cosmic muon (sum of all PMTs detecting a S1 signal) and observed light yield (average value from events and maximum value reached in an event) for two trigger modes. All the values obtained from data. Trigger | PMTs | S1 charge | Obs. light yield
---|---|---|---
|  | per muon (PE) | (PE/MeV)
|  |  | Avg. | Max.
| All | 1830 | 2.3 | 21.4
Random | 6 TPB | 650 | 0.8 | 10.2
| 30 PEN | 1180 | 1.5 | 13.1
| All | 1320 | 0.7 | 2.1
CRT | 6 TPB | 590 | 0.3 | 1.1
| 30 PEN | 730 | 0.4 | 1.0
Figure 22: Normalized distributions of the total S1 charge per cosmic muon
event (sum of all detecting PMTs) in random trigger. Both data and MC (TPB and
PEN) cases are displayed. The ADC saturation in data that limits the charge
range is fairly reproduced in the MC.

According to the values in Table 8, the complete PDS of ProtoDUNE-DP (36 PMTs)
detects about 2.3 PE/MeV and 0.7 PE/MeV for random-trigger and CRT-trigger
muon tracks, respectively. The latter case corresponds to an average track-PMT
distance of 475 cm whereas the former comprises muons crossing the detector at
all distances from the PMTs (from close to distant tracks, up to 7 m away from
the PMTs). Since the scintillation photons from cosmic muons triggered with
the CRTs are produced farther from the PMTs, the light is expected to undergo
more attenuation. It is worth commenting that the difference between the
average and maximum observed light yields is wider for random trigger (almost
10 times) than for CRT-trigger events (3 times) due to the larger variety of
events (energies and track topologies) in the first case.

The light yield is also shown in Table 8 separately for the two WLS groups in
order to highlight the fact that the 6 TPB PMTs alone collect 1/3 of the total
light. It can be concluded that if the 36 PMTs had TPB coating (PEN foil), a
detection of about 5 PE/MeV (2 PE/MeV) would be reached in random trigger, on
average. Therefore, in this particular PDS configuration, the use of PEN as
the only WLS option would compromise the capability of the system as a
calorimeter for low-energy particles. Considering TPB as the baseline WLS
would make the low-energy physics goals of the DUNE Far Detector, such as
triggering on a supernova neutrino burst, more feasible to accomplish.

In conclusion, the better detection efficiency of the TPB-coated PMTs together
with their stable performance during the detector operation confirm that TPB
is a better WLS choice for future LArTPCs. However, the mechanical advantages
of PEN make it a good candidate if the emission efficiency is not critical.

##  7 Electroluminescence light detection

Figure 23: Example of an event with S1 and S2 signals showing the waveforms
corresponding to the 36 PMTs according to their relative position in the
detector. PEN PMTs are shown in black and TPB PMTs in red. The $x$-axis range
is 0.8$-$3 ms for all PMTs while the $y$-axis range varies for each PMT and is
optimized to best display the S2 signal.  Figure 24: Example of a
scintillation light event in a ProtoDUNE-DP PMT at a gain of $10^{7}$ in LAr
with drift, amplification and extraction fields (S1 and S2 signal). The
acquired waveform is shown in black and the resulting waveform after the
overshooting correction in red. Figure 25: Average waveform of PEN PMTs with a
S2 signal identified. The $y$-axis is zoomed to focus on the S2 signal.

The electrons extracted into the gas phase produce produces a secondary
scintillation signal, called S2, which is proportional to the drifted charge.
The S2 signal provides information on the drifted electrons and the track
topology. For instance, the time spread of the S2 signal is related to the
track topology, as the more inclined the track is, the larger the S2 time
spread is. The time difference between the S2 signal and the S1 peak
corresponds to the drift time that takes to the electrons produced by the
ionizing particles to the anode.

In ProtoDUNE-DP, S2 electroluminescence signals are detected in all PMTs when
extraction and amplification fields are on, corresponding to light originating
about 7 m away from the PMTs. This detection is achieved thanks to the good
LAr optical properties and purity and the high efficiency of the ProtoDUNE-DP
PDS. In Fig. 23 an example event with S2 signal detected by all PMTs is shown.
This is the first time light produced at such a large distance has been
detected in a LArTPC.

ProtoDUNE-DP operated on the surface and observed a high S1 rate, see section
6. The S2 signals are mostly observed as an increase in SPE rate which
increases from hundreds of kHz, see section 3.2, up to several MHz ($\sim$2.5
MHz for TPB PMTs and $\sim$1.1 MHz for PEN PMTs) when there is S2 light
production (DP operation mode). It is clear that the S2 signals are an
important and continuous contribution to the low-energy background in the PMT
waveform when the extraction field is on.

As a result, not every S2 signal can be associated to its previous S1. Only
very energetic events produce S2 signals that can be distinguished from the
SPE background and associated to their previous S1. A dedicated algorithm was
developed to select these events where the S2 signal can be distinguished from
the SPE background and evaluate them in relation to their previous S1 signal.

An individual PMT waveform of a very energetic event selected by the algorithm
can be seen in Fig. 24. In this case, a fast S1 signal can be seen followed by
a S2 signal with a duration of $\sim$2 ms. Other S1 signals from cosmic muons
are also visible. The S2 signals are observed to cause an overshoot in the
waveforms as the amount of charge collected in the PMT anode exceeds the
discharging rate of the combined PMT and readout circuit (1/$RC$ constant),
effectively shifting the waveform baseline during the pulse. Waveforms are
processed offline to correct for this effect, as described in 311light . In
Fig. 24 the PMT waveform before and after the overshooting correction is
shown.

Fig. 25 shows the average waveform of the events passing the algorithm. These
are very energetic events corresponding mainly to vertical showers where the
track passed through the liquid-gas interface, and the S2 maximum is produced
right after the S1. In these events the average S1 charge detected per PMT is
$>$700 PE and the S2 charge $>$30 kPE. S2 signals last $\sim$2 ms. As
illustrated in Fig. 3, vertical muons will traverse a field of $\sim$0.2 kV/cm
along the first two meters for which an electron drift velocity of $\sim$1
mm/$\mu$s is expected Lastoria:2748990 . This implies an expected S2 duration
of $\sim$2 ms, which is consistent with the observed time.

##  8 Scintillation light in Xe-doped LAr

The use of Xe-doped LAr is a promising alternative to pure LAr for large-scale
LArTPCs, since it mitigates the light suppression due to some impurities and
it also improves the detection efficiency and uniformity.

Figure 26: Evolution of the average S1 charge per event detected in all the
PMTs for PMT(CRT)-trigger data in black (red) during the evaporation, filling
and N2 injections. The processes are described in section 2.

In the presence of Xe, molecular Ar excimers in the triplet state live long
enough to transfer their energy to the Xe atoms creating Xe excimers that
decay and produce photons with a longer wavelength than 127 nm KUBOTA199371 .
Therefore, the scintillation light is no longer monochromatic and has three
components: Ar light at 127 nm, and Xe light at 178 nm and 150 nm, with
significant contribution at 150 nm only at Xe concentrations below 1 ppm
Neumeier_2015_ArXe . Since molecular Ar excimers in the singlet state decay
too fast to transfer their energy, they produce 127-nm photons only, while the
late light is dominated by 150-nm and 178-nm photons.

Xe doping also affects the photon propagation. On one hand, it is reported
that Xe acts as an impurity, suppressing part of the spectrum at 127 nm even
at the concentration of 0.1 ppm Neumeier_2015_Attenuation . This absorption
reduces the fast component of the detected signal. On the other hand, photons
at longer wavelengths (150 and 178 nm) are not absorbed
Neumeier_2015_Attenuation , and have a longer RSL. RSL is $\sim$1 m for 127-nm
photons while it is $\sim$3.5 m for 150 nm and $\sim$9 m for 178 nm photons
Babicz:2020den . A longer RSL enhances the light detection at longer distances
with respect to pure LAr, since photons with a longer RSL scatter less and are
able to travel further thereby improving the uniformity of detection.

The presence of nitrogen in LAr leads to the suppression of the light
production due to a quenching process driven by two-body collisions of $N_{2}$
impurities with excited argon excimers Acciarri_N2 . This quenching affects
mainly the excimers in the triplet state as the singlet state decays much
faster. Since quenching by N2 and Xe excimer formation are competing
processes, the Xe atoms mitigate the light suppression due to the N2.

Additionally, the absorption of photons by N2 is expected to increase with the
concentration, with a reported absorption length for 127-nm photons of $28$ m
at 1.7 ppm, 20 m at 2.7 ppm and 12 m at 4.7 ppm Jones:2013bca .

In the case of ProtoDUNE-DP, after the re-filling with $\sim$230 ton of Xe-
doped liquid argon contaminated with N2, as described in section 2, both N2
and Xe species were present in the LAr, altering the light production and
propagation. The effect of the presence of Xe and N2 in the detected light in
ProtoDUNE-DP is studied using two types of muon-track signals: First, events
triggered with a TPB-coated PMT placed at the center of the detector with a
minimum amplitude of 25 PEs, for which the light is produced at a close
distance from the PMTs, and second, CRT-trigger events, for which the PMT-
track distance is in the range of 3$-$5 m.

Fig. 26 shows the evolution of the total S1 charge detected by all PMTs as a
function of time during the LAr evaporation from 7.4 m to 5.1 m of liquid
level, re-filling and N2 injection steps. Around 12 kPEs are detected on
average per PMT-trigger event, compared to only 2 kPEs per CRT-trigger event
in LAr. The S1 signal is integrated over a long time window of 12 $\mu$s after
the maximum. The reduction of the collected light during the evaporation is
visible for the CRT-trigger events, as part of the CRT-track is no longer in
the liquid, while the collected light for the PMT-trigger tracks is stable.
After the filling with Xe-doped LAr, the collected light increases as expected
due to the longer RSL, and it decreases with the N2 injections due to the
quenching.

Figure 27: Ratio of the average S1 amplitude and S1 charge in the three doping
concentrations relative to pure LAr. PMT-trigger data are shown in black, and
CRT-trigger in red. Only a selection of PMTs is considered for the average.
Selected PMTs are marked as black (red) circles in the small diagram for the
PMT(CRT)-trigger data. A blue circle marks the trigger PMT in the PMT-trigger
diagram. Errors show the STD among the selected PMTs. Xe and N2 concentrations
for each situation are summarized in Table 1.

To quantify the impact of the presence of Xe and N2 on the S1 charge and
amplitude with respect to their values in pure LAr, data taken with the same
liquid level are selected to ensure the same optical conditions. Fig. 27 shows
the average variation of the S1 amplitude and S1 charge for different detector
conditions. Only PMTs placed near the trigger PMT are considered for PMT-
trigger data analysis, to have a similar track-PMT distance, while only
channels placed below the CRT-track are selected for CRT-trigger data
analysis, since the other PMTs are placed more than five meters away from the
track and for these distant PMTs the signal is dominated by background. The
amplitude decreases 35% when adding 5.8 ppm of Xe and 2.4 ppm of N2 with
respect to pure LAr for both trigger modes, being unaffected by the N2
addition. This reduction is due to the absorption of the 127-nm photons by the
Xe atoms as reported in Neumeier_2015_Attenuation . Whilst a 16% decrease of
the fast component (S1 amplitude) for the CRT-trigger data is expected when
adding N2 according to Jones:2013bca , we observe the fast component remaining
constant for the two N2 injections. A possible explanation for this
discrepancy is that the assumption of the fast component being monochromatic
at 127 nm is not accurate, and an additional light contribution at a different
wavelength, that is not absorbed by the N2, is masking the expected absorption
at 127 nm. The collected S1 charge increases 100% for the CRT-trigger data,
while only 50% for the PMT-trigger data. This difference is understood as an
improvement of the detection uniformity, since CRT-trigger muons are on
average farther away from the PMTs, and the longer RSL of the Xe photons
improves their collection at large distances. The decrease of the S1 charge
due to the presence of N2 is similar for both triggers (30%), meaning that
there is no dependence of the detected light suppression on the PMT-track
distance. This indicates that the reduction is mainly due to the quenching of
the Ar excimers by N2 rather than photo-absorption.

In order to evaluate the effect of the Xe-doping on the attenuation length
($\lambda_{\mathrm{att}}$), a study of the dependence of the collected light
per PMT with the track-PMT distance is performed for muon tracks crossing the
CRT panels, see Fig. 28. The range of distances is given by the position of
the PMTs with respect to the triggered track, which is always approximately in
the same position (see section 1). The behavior is not purely exponential
since light absorption by the field cage introduces a border effect. A
shoulder shape is observed at around 4.3 m, which corresponds to the PMTs
placed at the center of the detector, where this effect is reduced. This
limitation is due to the fixed geometry of the triggered tracks.An exponential
fit is performed to estimate the attenuation length in each detector
condition, and the results are summarized in Table 9. The effective
attenuation length increases 60% when adding Xe, as expected because of the
longer RSL, and decreases 5% when adding 2.9 ppm of N2. The lower panel of
Fig. 28 shows the ratios of the top panel curves. The comparison between pure
LAr and Xe-doped LAr in red shows the improved detection uniformity when
adding Xe, with an increase of almost a factor of $\sim$3 on events at 5 m and
a factor of $\sim$2 on events at 3 m. The extrapolation of this curve to short
distances indicates an increase by a factor of 1.5 at 2.2 m, as seen in Fig.
27 for the PMT-trigger muons, and no increase at 0 m, as expected due to the
longer RSL. The flat blue and magenta lines show that there is no dependence
on the distance for the N2 injections, as seen also in Fig. 27.

Figure 28: Top panel: Average collected S1 charge per PMT as a function of the track-PMT distance for the different doping concentrations. For each data-set, an exponential fit is performed (see results in Table 9). Bottom panel: Ratios between the data shown in the top panel. Each data-set is fitted with an exponential function. Table 9: Measured attenuation lengths in the different doping concentrations from the exponential fits shown in Fig. 28. Situation | [Xe](ppm) | [N2] (ppm) |  $\lambda_{\mathrm{att}}$ (cm)
---|---|---|---
LAr | 0 | 0 | $209\pm 5$
LAr + Xe + N2 | 5.8 | 2.4 | $338\pm 13$
1st N2 inj. | 5.8 | 3.4 | $332\pm 13$
2nd N2 inj. | 5.8 | 5.3 | $321\pm 12$

The effect of xenon on the time profile of the waveforms is also studied. Fig.
29 shows the average waveforms for a TPB-coated PMT in the different doping
concentrations described in section 2 (pure LAr in black), normalized to the
same amplitude. The average waveform is the result of adding waveforms for a
PMT placed near the trigger PMT. The second bump in the red, blue and magenta
waveforms (when Xe is present) is the late light at 150 nm and 178 nm, as
explained before. The profile of this second maximum changes with the N2
injections, as the energy transfer rate from argon to xenon atoms is altered
due to the quenching by N2. The average waveforms of Xe-doped LAr are fitted
to the sum of three exponential functions convolved with a Gaussian to account
for the PMT response, in a similar way as in Eq. 6. In this case, the first
exponential describes the fast signal, and the second and third exponential
functions model the rise ($\tau_{\mathrm{transfer}}$) and decay
($\tau_{\mathrm{slow}}$) of the second bump from the Xe light.

Figure 29: PMT average waveform normalized to same amplitude in LAr (black),
LAr + Xe + N2 (red), after the 1st N2 injection (blue) and after the 2nd N2
injection (magenta). Concentrations are described in Table 9.

The time constants obtained from the fits are shown in Table 10. No difference
between PEN-foil and TPB-coated PMTs is found.

Table 10: Time constants obtained from the fit of the average waveforms at the different doping concentrations. Errors show the variation among PMTs. Situation |  $\tau_{\mathrm{transfer}}$ ($\pm 0.01\mu$s) |  $\tau_{\mathrm{slow}}$ ($\pm 0.01\mu$s)
---|---|---
LAr | N/A | $1.43$
LAr + Xe + N2 | $0.46$ | $1.10$
1st N2 injection | $0.41$ | $1.03$
2nd N2 injection | $0.35$ | $0.91$

In summary, ProtoDUNE-DP data show that Xe doping is a promising technique for
large-scale detectors like DUNE since it increases the collected light at
large distances. With a small doping level of 5.8 ppm of Xe (and even with the
presence of 2.4 ppm of N2), an enhancement in the light detection efficiency
(100% increase for muons crossing at a distance of 3$-$5 m from the PMTs) and
a better uniformity (attenuation length 50% longer) is measured. However,
despite these advantages, it must be considered that the 35% amplitude
reduction observed in the fast signal could jeopardize the efficiency of a
light-based trigger.

##  9 Conclusions

ProtoDUNE-DP is a 6$\times$6$\times$6 m3 LArTPC, operated at CERN between 2019
and 2020 to demonstrate the dual-phase technology at large scale for DUNE, a
next generation long-baseline neutrino experiment. The photon detection system
is composed of 36 8-inch cryogenic PMTs from Hamamatsu positioned at the
bottom of the detector. The photon detection system collected cosmic-ray data
for 18 months in stable conditions with all 36 PMTs in operation. The good
performance validates the photon detection system design for future long drift
distance LArTPCs.

ProtoDUNE-DP used PEN as a wavelength shifter for the first time in a large
scale experiment and a comparison with the widely used TPB is carried out. TPB
is estimated to be 3 times more efficient than PEN. The observed light yield
from cosmic muons demonstrates that a system based exclusively on TPB as WLS
would be needed to accomplish the DUNE low-energy physics program goals.
Nonetheless, PEN can be taken into account as an alternative when the
detection efficiency is not critical compared to the benefits of easy
installation.

In ProtoDUNE-DP, considering the field limitations, it is found that at least
17% of the scintillation light detected in the absence of a drift field comes
from electron-ion recombination, verifying the expected trend from Birks’ law
and in agreement with previous work. A decrease of $\tau_{\mathrm{slow}}$ with
the drift field as reported in 311light  is also observed. An explanation of
this effect is provided in Segreto:2020qks  taking into account the quenching
of the long lived triplet states through the self-interaction with other
triplet states or through the interactions with molecular argon ions.

The size of ProtoDUNE-DP allows for an unprecedented study of the light
propagation. An evaluation of the Rayleigh scattering length is carried out by
comparing the measured light signals with the light predicted by the MC
simulation testing two lengths (61.0 cm and 99.9 cm). The agreement between
data and the 99.9-cm MC sample is better than for the shorter scattering
length. It is also concluded that at least 11% of the light detected by PMTs
in the MC corresponds to VUV-light reflected off the field cage, cryostat
walls, cathode and ground grid.

The cosmic muon flux in ProtoDUNE-DP at ground level is determined from the S1
signal rate detected by the PMTs and a cosmic-muon light simulation sample.
The result, 148${}^{+8}_{-11}$ Hz/m2, is consistent with other muon flux
measurements in the literature microboone2021 ; mitrica2011 .

The electroluminescence light, S2, produced in the gas phase about 7 m away
from the PMTs is observed in all 36 PMTs implying a high efficiency for the
ProtoDUNE-DP photon detection system. The detected SPE rate increases up to
several MHz when there is S2 light production, clear evidence that the S2
signals are an important and continuous contribution to the low-energy
background in the PMT waveform.

Finally, ProtoDUNE-DP data has demonstrated the improvement of the light
detection efficiency and uniformity in large LArTPCs, thanks to the Xe doping.
A low doping level of 5.8 ppm of Xe doubles the collected light at large
distances (3$-$5 m from the PMTs) even with the presence of 2.4 ppm of N2.
However, it must be considered that the reduction observed in the fast signal
amplitude could compromise the performance of a light-based trigger.

###### Acknowledgements.

The ProtoDUNE-DP 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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