Resumen
The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eV up to more than 1020 eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.
| Idioma original | Inglés |
|---|---|
| Número de artículo | P07016 |
| Publicación | Journal of Instrumentation |
| Volumen | 16 |
| N.º | 7 |
| DOI | |
| Estado | Publicada - jul. 2021 |
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En: Journal of Instrumentation, Vol. 16, N.º 7, P07016, 07.2021.
Resultado de la investigación: Contribución a una revista › Artículo › revisión exhaustiva
TY - JOUR
T1 - Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks
AU - Aab, A.
AU - Abreu, P.
AU - Aglietta, M.
AU - Albury, J. M.
AU - Allekotte, I.
AU - Almela, A.
AU - Alvarez-Muñiz, J.
AU - Batista, R. Alves
AU - Anastasi, G. A.
AU - Anchordoqui, L.
AU - Andrada, B.
AU - Andringa, S.
AU - Aramo, C.
AU - Ferreira, P. R.Araújo
AU - Velázquez, J. C.Arteaga
AU - Asorey, H.
AU - Assis, P.
AU - Avila, G.
AU - Badescu, A. M.
AU - Bakalova, A.
AU - Balaceanu, A.
AU - Barbato, F.
AU - Luz, R. J.Barreira
AU - Becker, K. H.
AU - Bellido, J. A.
AU - Berat, C.
AU - Bertaina, M. E.
AU - Bertou, X.
AU - Biermann, P. L.
AU - Bister, T.
AU - Biteau, J.
AU - Blazek, J.
AU - Bleve, C.
AU - Boháčová, M.
AU - Boncioli, D.
AU - Bonifazi, C.
AU - Arbeletche, L. Bonneau
AU - Borodai, N.
AU - Botti, A. M.
AU - Brack, J.
AU - Bretz, T.
AU - Orchera, P. G.Brichetto
AU - Briechle, F. L.
AU - Buchholz, P.
AU - Bueno, A.
AU - Buitink, S.
AU - Buscemi, M.
AU - Caballero-Mora, K. S.
AU - Caccianiga, L.
AU - Canfora, F.
AU - Caracas, I.
AU - Carceller, J. M.
AU - Caruso, R.
AU - Castellina, A.
AU - Catalani, F.
AU - Cataldi, G.
AU - Cazon, L.
AU - Cerda, M.
AU - Chinellato, J. A.
AU - Choi, K.
AU - Chudoba, J.
AU - Chytka, L.
AU - Clay, R. W.
AU - Cerutti, A. C.Cobos
AU - Colalillo, R.
AU - Coleman, A.
AU - Coluccia, M. R.
AU - Conceição, R.
AU - Condorelli, A.
AU - Consolati, G.
AU - Contreras, F.
AU - Convenga, F.
AU - dos Santos, D. Correia
AU - Covault, C. E.
AU - Dasso, S.
AU - Daumiller, K.
AU - Dawson, B. R.
AU - Day, J. A.
AU - de Almeida, R. M.
AU - de Jesús, J.
AU - de Jong, S. J.
AU - de Mauro, G.
AU - de Mello Neto, J. R.T.
AU - de Mitri, I.
AU - de Oliveira, J.
AU - de Oliveira Franco, D.
AU - de Palma, F.
AU - de Souza, V.
AU - de Vito, E.
AU - del Río, M.
AU - Deligny, O.
AU - Di Matteo, A.
AU - Dobrigkeit, C.
AU - D’Olivo, J. C.
AU - dos Anjos, R. C.
AU - Dova, M. T.
AU - Ebr, J.
AU - Engel, R.
AU - Epicoco, I.
AU - Erdmann, M.
AU - Escobar, C. O.
AU - Etchegoyen, A.
AU - Falcke, H.
AU - Farmer, J.
AU - Farrar, G.
AU - Fauth, A. C.
AU - Fazzini, N.
AU - Feldbusch, F.
AU - Fenu, F.
AU - Fick, B.
AU - Figueira, J. M.
AU - Filipčič, A.
AU - Fodran, T.
AU - Freire, M. M.
AU - Fujii, T.
AU - Fuster, A.
AU - Galea, C.
AU - Galelli, C.
AU - García, B.
AU - Vegas, A. L.Garcia
AU - Gemmeke, H.
AU - Gesualdi, F.
AU - Gherghel-Lascu, A.
AU - Ghia, P. L.
AU - Giaccari, U.
AU - Giammarchi, M.
AU - Giller, M.
AU - Glombitza, J.
AU - Gobbi, F.
AU - Gollan, F.
AU - Golup, G.
AU - Berisso, M. Gómez
AU - Vitale, P. F.Gómez
AU - Gongora, J. P.
AU - González, J. M.
AU - González, N.
AU - Goos, I.
AU - Góra, D.
AU - Gorgi, A.
AU - Gottowik, M.
AU - Grubb, T. D.
AU - Guarino, F.
AU - Guedes, G. P.
AU - Guido, E.
AU - Hahn, S.
AU - Hamal, P.
AU - Hampel, M. R.
AU - Hansen, P.
AU - Harari, D.
AU - Harvey, V. M.
AU - Haungs, A.
AU - Hebbeker, T.
AU - Heck, D.
AU - Hill, G. C.
AU - Hojvat, C.
AU - Hörandel, J. R.
AU - Horvath, P.
AU - Hrabovský, M.
AU - Huege, T.
AU - Hulsman, J.
AU - Insolia, A.
AU - Isar, P. G.
AU - Janecek, P.
AU - Johnsen, J. A.
AU - Jurysek, J.
AU - Kääpä, A.
AU - Kampert, K. H.
AU - Keilhauer, B.
AU - Kemp, J.
AU - Klages, H. O.
AU - Kleifges, M.
AU - Kleinfeller, J.
AU - Köpke, M.
AU - Kunka, N.
AU - Lago, B. L.
AU - Lang, R. G.
AU - Langner, N.
AU - de Oliveira, M. A.Leigui
AU - Lenok, V.
AU - Letessier-Selvon, A.
AU - Lhenry-Yvon, I.
AU - Lo Presti, D.
AU - Lopes, L.
AU - López, R.
AU - Lu, L.
AU - Luce, Q.
AU - Lucero, A.
AU - Lundquist, J. P.
AU - Payeras, A. Machado
AU - Mancarella, G.
AU - Mandat, D.
AU - Manning, B. C.
AU - Manshanden, J.
AU - Mantsch, P.
AU - Marafico, S.
AU - Mariazzi, A. G.
AU - Mariş, I. C.
AU - Marsella, G.
AU - Martello, D.
AU - Martinez, H.
AU - Bravo, O. Martínez
AU - Mastrodicasa, M.
AU - Mathes, H. J.
AU - Matthews, J.
AU - Matthiae, G.
AU - Mayotte, E.
AU - Mazur, P. O.
AU - Medina-Tanco, G.
AU - Melo, D.
AU - Menshikov, A.
AU - Merenda, K. D.
AU - Michal, S.
AU - Micheletti, M. I.
AU - Miramonti, L.
AU - Mollerach, S.
AU - Montanet, F.
AU - Morello, C.
AU - Mostafá, M.
AU - Müller, A. L.
AU - Muller, M. A.
AU - Mulrey, K.
AU - Mussa, R.
AU - Muzio, M.
AU - Namasaka, W. M.
AU - Nasr-Esfahani, A.
AU - Nellen, L.
AU - Niculescu-Oglinzanu, M.
AU - Niechciol, M.
AU - Nitz, D.
AU - Nosek, D.
AU - Novotny, V.
AU - Nožka, L.
AU - Nucita, A.
AU - Núñez, L. A.
AU - Palatka, M.
AU - Pallotta, J.
AU - Papenbreer, P.
AU - Parente, G.
AU - Parra, A.
AU - Pech, M.
AU - Pedreira, F.
AU - Pekala, J.
AU - Pelayo, R.
AU - Peña-Rodriguez, J.
AU - Martins, E. E.Pereira
AU - Armand, J. Perez
AU - Bertolli, C. Pérez
AU - Perlin, M.
AU - Perrone, L.
AU - Petrera, S.
AU - Pierog, T.
AU - Pimenta, M.
AU - Pirronello, V.
AU - Platino, M.
AU - Pont, B.
AU - Pothast, M.
AU - Privitera, P.
AU - Prouza, M.
AU - Puyleart, A.
AU - Querchfeld, S.
AU - Rautenberg, J.
AU - Ravignani, D.
AU - Reininghaus, M.
AU - Ridky, J.
AU - Riehn, F.
AU - Risse, M.
AU - Rizi, V.
AU - de Carvalho, W. Rodrigues
AU - Rojo, J. Rodriguez
AU - Roncoroni, M. J.
AU - Roth, M.
AU - Roulet, E.
AU - Rovero, A. C.
AU - Ruehl, P.
AU - Saffi, S. J.
AU - Saftoiu, A.
AU - Salamida, F.
AU - Salazar, H.
AU - Salina, G.
AU - Gomez, J. D.Sanabria
AU - Sánchez, F.
AU - Santos, E. M.
AU - Santos, E.
AU - Sarazin, F.
AU - Sarmento, R.
AU - Sarmiento-Cano, C.
AU - Sato, R.
AU - Savina, P.
AU - Schäfer, C. M.
AU - Scherini, V.
AU - Schieler, H.
AU - Schimassek, M.
AU - Schimp, M.
AU - Schlüter, F.
AU - Schmidt, D.
AU - Scholten, O.
AU - Schovánek, P.
AU - Schröder, F. G.
AU - Schröder, S.
AU - Schulte, J.
AU - Sciutto, S. J.
AU - Scornavacche, M.
AU - Segreto, A.
AU - Sehgal, S.
AU - Shellard, R. C.
AU - Sigl, G.
AU - Silli, G.
AU - Sima, O.
AU - Šmída, R.
AU - Sommers, P.
AU - Soriano, J. F.
AU - Souchard, J.
AU - Squartini, R.
AU - Stadelmaier, M.
AU - Stanca, D.
AU - Stanič, S.
AU - Stasielak, J.
AU - Stassi, P.
AU - Streich, A.
AU - Suárez-Durán, M.
AU - Sudholz, T.
AU - Suomijärvi, T.
AU - Supanitsky, A. D.
AU - Šupík, J.
AU - Szadkowski, Z.
AU - Taboada, A.
AU - Tapia, A.
AU - Taricco, C.
AU - Timmermans, C.
AU - Tkachenko, O.
AU - Tobiska, P.
AU - Peixoto, C. J.Todero
AU - Tomé, B.
AU - Travaini, A.
AU - Travnicek, P.
AU - Trimarelli, C.
AU - Trini, M.
AU - Tueros, M.
AU - Ulrich, R.
AU - Unger, M.
AU - Vaclavek, L.
AU - Vacula, M.
AU - Galicia, J. F.Valdés
AU - Valore, L.
AU - Varela, E.
AU - Varma, V.
AU - Vásquez-Ramírez, A.
AU - Veberič, D.
AU - Ventura, C.
AU - Quispe, I. D.Vergara
AU - Verzi, V.
AU - Vicha, J.
AU - Vink, J.
AU - Vorobiov, S.
AU - Wahlberg, H.
AU - Watanabe, C.
AU - Watson, A. A.
AU - Weber, M.
AU - Weindl, A.
AU - Wiencke, L.
AU - Wilczyński, H.
AU - Winchen, T.
AU - Wirtz, M.
AU - Wittkowski, D.
AU - Wundheiler, B.
AU - Yushkov, A.
AU - Zapparrata, O.
AU - Zas, E.
AU - Zavrtanik, D.
AU - Zavrtanik, M.
AU - Zehrer, L.
AU - Zepeda, A.
N1 - Funding Information: Argentina ? Comisi?n Nacional de Energ?a At?mica; Agencia Nacional de Promoci?n Cient?fica y Tecnol?gica (ANPCyT); Consejo Nacional de Investigaciones Cient?ficas y T?cnicas (CONICET); Gobierno de la Provincia de Mendoza; Municipalidad de Malarg?e; NDM Holdings and Valle Las Le?as; in gratitude for their continuing cooperation over land access; Australia ? the Australian Research Council; Brazil ? Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq); Financiadora de Estudos e Projetos (FINEP); Funda??o de Amparo ? Pesquisa do Estado de Rio de Janeiro (FAPERJ); S?o Paulo Research Foundation (FAPESP) Grants No. 2019/10151-2, No. 2010/07359-6 and No. 1999/05404-3; Minist?rio da Ci?ncia, Tecnologia, Inova??es e Comunica??es (MCTIC); Czech Republic ? Grant No. MSMT CR LTT18004, LM2015038, LM2018102, CZ.02.1.01/0.0/0.0/16_013/0001402, CZ.02.1.01/0.0/0.0/18_046/0016010 and CZ.02.1.01/0.0/0.0/17_049/0008422; France ? Centre de Calcul IN2P3/CNRS; Centre National de la Recherche Scientifique (CNRS); Conseil R?gional Ile-de-France; D?partement Physique Nucl?aire et Corpusculaire (PNC-IN2P3/CNRS); D?partement Sciences de l?Univers (SDU-INSU/CNRS); Institut Lagrange de Paris (ILP) Grant No. LABEX ANR-10-LABX-63 within the Investissements d?Avenir Programme Grant No. ANR-11-IDEX-0004-02; Germany ? Bundesministerium f?r Bildung und Forschung (BMBF); Deutsche Forschungsgemeinschaft (DFG); Finanzministerium Baden-W?rttemberg; Helmholtz Alliance for Astroparticle Physics (HAP); Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF); Ministerium f?r Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen; Ministerium f?r Wissenschaft, Forschung und Kunst des Landes Baden-W?rttemberg; Italy ? Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Astrofisica (INAF); Ministero dell?Istruzione, dell?Universit? e della Ricerca (MIUR); CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE); M?xico ? Consejo Nacional de Ciencia y Tecnolog?a (CONACYT) No. 167733; Universidad Nacional Aut?noma de M?xico (UNAM); PAPIIT DGAPA-UNAM; The Netherlands ? Ministry of Education, Culture and Science; Netherlands Organisation for Scientific Research (NWO); Dutch national e-infrastructure with the support of SURF Cooperative; Poland -Ministry of Science and Higher Education, grant No. DIR/WK/2018/11; National Science Centre, Grants No. 2013/08/M/ST9/00322, No. 2016/23/B/ST9/01635 and No. HARMONIA 5-2013/10/M/ST9/00062, UMO-2016/22/M/ST9/00198; Portugal ? Portuguese national funds and FEDER funds within Programa Operacional Factores de Competitividade through Funda??o para a Ci?ncia e a Tecnologia (COMPETE); Romania ? Romanian Ministry of Education and Research, the Program Nucleu within MCI (PN19150201/16N/2019 and PN19060102) and project PN-III-P1-1.2-PCCDI-2017-0839/19PCCDI/2018 within PNCDI III; Slovenia ? Slovenian Research Agency, grants P1-0031, P1-0385, I0-0033, N1-0111; Spain ? Ministerio de Econom?a, Industria y Competitividad (FPA2017-85114-P and PID2019-104676GB-C32, Xunta de Galicia (ED431C 2017/07), Junta de Andaluc?a (SOMM17/6104/UGR, P18-FR-4314) Feder Funds, RENATA Red Nacional Tem?tica de Astropart?culas (FPA2015-68783-REDT) and Mar?a de Maeztu Unit of Excellence (MDM-2016-0692); U.S.A. ? Department of Energy, Contracts No. DE-AC02-07CH11359, No. DE-FR02-04ER41300, No. DE-FG02-99ER41107 and No. DE-SC0011689; National Science Foundation, Grant No. 0450696; The Grainger Foundation; Marie Curie-IRSES/EPLANET; European Particle Physics Latin American Network; and UNESCO. Publisher Copyright: © 2021 IOP Publishing Ltd and Sissa Medialab.
PY - 2021/7
Y1 - 2021/7
N2 - The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eV up to more than 1020 eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.
AB - The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eV up to more than 1020 eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.
KW - Analysis and statistical methods
KW - Calibration and fitting methods
KW - Cherenkov detectors
KW - Cluster finding
KW - Large detector systems for particle and astroparticle physics
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85110748213&partnerID=8YFLogxK
U2 - 10.1088/1748-0221/16/07/P07016
DO - 10.1088/1748-0221/16/07/P07016
M3 - Artículo
AN - SCOPUS:85110748213
SN - 1748-0221
VL - 16
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 7
M1 - P07016
ER -