Abstract
The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV.
| Original language | English (US) |
|---|---|
| Article number | P07019 |
| Journal | Journal of Instrumentation |
| Volume | 16 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2021 |
All Science Journal Classification (ASJC) codes
- Instrumentation
- Mathematical Physics
Access to Document
Other files and links
Fingerprint
Dive into the research topics of 'Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver
}
In: Journal of Instrumentation, Vol. 16, No. 7, P07019, 07.2021.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory
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 - Presti, D. Lo
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 - Pękala, 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 - Rodrigues de Carvalho, W.
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. K.C.
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: The successful installation, commissioning, and operation of the Pierre Auger Observatory would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support. Argentina - Comisión Nacional de Energía Atómica; Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT); Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Gobierno de la Provincia de Mendoza; Municipalidad de Malargüe; NDM Holdings and Valle Las Leñas; in gratitude for their continuing cooperation over land access; Australia - the Australian Research Council; Brazil - Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Financiadora de Estudos e Projetos (FINEP); Fundação de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ); São Paulo Research Foundation (FAPESP) Grants No. 2019/10151-2, No. 2010/07359-6 and No. 1999/05404-3; Ministério da Ciência, Tecnologia, Inovações e Comunicações (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 Régional Ile-de-France; Département Physique Nucléaire et Corpusculaire (PNC-IN2P3/CNRS); Département 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 für Bildung und Forschung (BMBF); Deutsche Forschungsgemeinschaft (DFG); Finanzministerium Baden-Württemberg; Helmholtz Alliance for Astroparticle Physics (HAP); Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF); Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen; Ministerium für Wissenschaft, Forschung und Kunst des Landes Baden-Württemberg; Italy - Istituto Nazionale di Fisica Nucleare (INFN); Istituto Nazionale di Astrofisica (INAF); Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR); CETEMPS Center of Excellence; Ministero degli Affari Esteri (MAE); México - Consejo Nacional de Ciencia y Tecnología (CONACYT) No. 167733; Universidad Nacional Autónoma de México (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 Fundação para a Ciência 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 Economía, Industria y Competitividad (FPA2017-85114-P and PID2019-104676GB-C32, Xunta de Galicia (ED431C 2017/07), Junta de Andalucía (SOMM17/6104/UGR, P18-FR-4314) Feder Funds, RENATA Red Nacional Temática de Astropartículas (FPA2015-68783-REDT) and María de Maeztu Unit of Excellence (MDM-2016-0692); USA - 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 The Author(s).
PY - 2021/7
Y1 - 2021/7
N2 - The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV.
AB - The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV.
UR - http://www.scopus.com/inward/record.url?scp=85111106773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111106773&partnerID=8YFLogxK
U2 - 10.1088/1748-0221/16/07/P07019
DO - 10.1088/1748-0221/16/07/P07019
M3 - Article
AN - SCOPUS:85111106773
SN - 1748-0221
VL - 16
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 7
M1 - P07019
ER -