Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

被引：17

作者：

Aab, A. ^{[41
,89
]}

Abreu, P. ^{[79
,80
]}

Aglietta, M. ^{[57
,59
]}

Albury, J. M. ^{[19
]}

Allekotte, I. ^{[1
,2
]}

Almela, A. ^{[15
,18
]}

Alvarez-Muniz, J. ^{[88
]}

Batista, R. Alves ^{[89
]}

Anastasi, G. A. ^{[57
,68
]}

Anchordoqui, L. ^{[96
]}

Andrada, B. ^{[15
]}

Andringa, S. ^{[79
,80
]}

Aramo, C. ^{[55
]}

Ferreira, P. R. Araujo ^{[47
]}

Arteaga Velazquez, J. C. ^{[73
]}

Asorey, H. ^{[15
]}

Assis, P. ^{[79
,80
]}

Avila, G. ^{[16
,17
]}

Badescu, A. M. ^{[83
]}

Bakalova, A. ^{[37
]}

Balaceanu, A. ^{[81
]}

Barbato, F. ^{[41
,50
,51
]}

Barreira Luz, R. J. ^{[79
,80
]}

Becker, K. H. ^{[43
]}

Bellido, J. A. ^{[19
,75
]}

Berat, C. ^{[41
]}

Bertaina, M. E. ^{[57
,68
]}

Bertou, X. ^{[1
,2
]}

Biermann, P. L. ^{[106
]}

Bister, T. ^{[47
]}

Biteau, J. ^{[42
]}

Blazek, J. ^{[37
]}

Bleve, C. ^{[41
]}

Bohacova, M. ^{[37
]}

Boncioli, D. ^{[51
,62
]}

Bonifazi, C. ^{[31
]}

Bonneau Arbeletche, L. ^{[26
]}

Borodai, N. ^{[76
]}

Botti, A. M. ^{[15
]}

Brack, J. ^{[108
]}

Bretz, T. ^{[47
]}

Brichetto Orchera, P. G. ^{[15
]}

Briechle, F. L. ^{[47
]}

Buchholz, P. ^{[49
]}

Bueno, A. ^{[86
,87
]}

Buitink, S. ^{[21
]}

Buscemi, M. ^{[52
]}

Caballero-Mora, K. S. ^{[72
]}

Caccianiga, L. ^{[54
,64
]}

Canfora, F. ^{[89
,91
]}

机构：

[1] UNCuyo, CNEA, CONICET, Ctr Atom Bariloche, San Carlos De Bariloche, Rio Negro, Argentina

[2] UNCuyo, CNEA, CONICET, Inst Balseiro, San Carlos De Bariloche, Rio Negro, Argentina

[3] CITEDEF, Ctr Invest Laseres & Aplicac, Villa Martelli, Argentina

[4] Consejo Nacl Invest Cient & Tecn, Villa Martelli, Argentina

[5] Univ Buenos Aires, Dept Fis, FCEyN, Buenos Aires, DF, Argentina

[6] Univ Buenos Aires, Dept Ciencias Atmosfera & Oceanos, FCEyN, Buenos Aires, DF, Argentina

[7] Consejo Nacl Invest Cient & Tecn, Buenos Aires, DF, Argentina

[8] Univ Nacl La Plata, IFLP, La Plata, Argentina

[9] Consejo Nacl Invest Cient & Tecn, La Plata, Argentina

[10] UBA, CONICET, Inst Astron & Fis Espacio, Buenos Aires, DF, Argentina

[11] UNR, CONICET, Inst Fis Rosario IFIR, Rosario, Argentina

[12] UNR, Fac Ciencias Bioquim & Farmaceut, Rosario, Argentina

[13] UNSAM, CONICET, CNEA, Inst Tecnol Detecc & Astroparticulas, Mendoza, Argentina

[14] Univ Tecnol Nacl, Fac Reg Mendoza, CNEA, CONICET, Mendoza, Argentina

[15] UNSAM, Inst Tecnol Detecc & Astroparticulas, CNEA, CONICET, Buenos Aires, DF, Argentina

[16] Observatorio Pierre Auger, Malargue, Argentina

[17] Comis Nacl Energia Atom, Malargue, Argentina

[18] Univ Tecnol Nacl, Fac Reg Buenos Aires, Buenos Aires, DF, Argentina

[19] Univ Adelaide, Adelaide, SA, Australia

[20] Univ Libre Bruxelles, Brussels, Belgium

[21] Vrije Univ Brussels, Brussels, Belgium

[22] Ctr Brasileiro Pesquisas Fis, Rio De Janeiro, RJ, Brazil

[23] Ctr Fed Educ Tecnol Celso Suckow da Fonseca, Nova Friburgo, Brazil

[24] Univ Sao Paulo, Escola Engn Lorena, Lorena, SP, Brazil

[25] Univ Sao Paulo, Inst Fis Sao Carlos, Sao Carlos, SP, Brazil

[26] Univ Sao Paulo, Inst Fis, Sao Paulo, SP, Brazil

[27] Univ Estadual Campinas, IFGW, Campinas, SP, Brazil

[28] Univ Estadual Feira de Santana, Feira De Santana, BA, Brazil

[29] Univ Fed ABC, Santo Andre, SP, Brazil

[30] Univ Fed Parana, Setor Palotina, Palotina, Brazil

[31] Univ Fed Rio de Janeiro, Inst Fis, Rio De Janeiro, RJ, Brazil

[32] Univ Fed Rio de Janeiro, Observatorio Valongo, Rio De Janeiro, RJ, Brazil

[33] Univ Fed Fluminense, EEIMVR, Volta Redonda, RJ, Brazil

[34] Univ Medellin, Medellin, Colombia

[35] Univ Ind Santander, Bucaramanga, Colombia

[36] Charles Univ Prague, Inst Particle & Nucl Phys, Fac Math & Phys, Prague, Czech Republic

[37] Czech Acad Sci, Inst Phys, Prague, Czech Republic

[38] Palacky Univ, RCPTM, Olomouc, Czech Republic

[39] Univ Paris Saclay, CNRS, IN2P3, IJCLab, Orsay, France

[40] Univ Paris, CNRS, IN2P3, Lab Phys Nucl & Hautes Energies LPNHE,Sorbonne Un, Paris, France

[41] Univ Grenoble Alpes, CNRS, Grenoble Inst Engn, LPSC,IN2P3, Grenoble, France

[42] Univ Paris Saclay, CNRS, IJCLab, IN2P3, Orsay, France

[43] Berg Univ Wuppertal, Dept Phys, Wuppertal, Germany

[44] Karlsruhe Inst Technol, Inst Expt Particle Phys, Karlsruhe, Germany

[45] Karlsruhe Inst Technol, Inst Prozessdatenverarbeitung & Elekt, Karlsruhe, Germany

[46] Karlsruhe Inst Technol, Inst Astroparticle Phys, Karlsruhe, Germany

[47] Rhein Westfal TH Aachen, Phys Inst A 3, Aachen, Germany

[48] Univ Hamburg, Inst Theoret Phys 2, Hamburg, Germany

[49] Univ Siegen, Dept Phys Expt Teilchenphys, Siegen, Germany

[50] Gran Sasso Sci Inst, Laquila, Italy

来源：

JOURNAL OF INSTRUMENTATION | 2021年 / 16卷 / 07期

基金：

巴西圣保罗研究基金会; 美国国家科学基金会; 澳大利亚研究理事会;

关键词：

Data analysis; Pattern recognition; cluster finding; calibration and fitting methods; Large detector systems for particle and astroparticle physics; Particle identification methods;

D O I：

10.1088/1748-0221/16/07/P07019

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

The atmospheric depth of the air shower maximum X-max is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of X-max 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 X-max 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 X-max 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 X-max 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/cm(2) at energies above 2x10(19) eV.

引用

页数：29

共 22 条

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Glombitza, Jonas
Auger, Pierre
37TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2021, 2022,
[2] Reconstruction of Events Recorded with the Water-Cherenkov and Scintillator Surface Detectors of the Pierre Auger Observatory
Schmidt, David
37TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2021, 2022,
[3] Boosting the Performance of the Neural Network Using Symmetry Properties for the Prediction of the Shower Maximum Using the water-Cherenkov Detectors of the Pierre Auger Observatory as an Example
Hahn, Steffen
Roth, Markus
Schmidt, David
Veberic, Darko
37TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2021, 2022,
[4] Studies of signal waveforms from the water-Cherenkov detectors of the Pierre Auger Observatory
Allison, P. S.
Bui-Duc, H.
Chye, J.
Dagoret-Campagne, S.
Dorofeev, A.
Matthews, J.
Nitz, D. F.
Ranchon, S.
Urban, M.
Veberic, D.
Watson, A. A.
Wileman, C.
Proceedings of the 29th International Cosmic Ray Conference Vol 7: HE 1.4, 2005, : 357 - 360
[5] Air-Shower Reconstruction at the Pierre Auger Observatory based on Deep Learning
Glombitza, Jonas
36TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2019, 2021,
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Todero Peixoto, Carlos Jose
36TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2019, 2021,
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Aglietta, M.
Allison, P.
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Arneodo, F.
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Moreno, E.
Perez, G.
Salazar, H.
Proceedings of the 29th International Cosmic Ray Conference Vol 7: HE 1.4, 2005, : 83 - 86
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Aab, A.
Abreu, P.
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Allekotte, I
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Alvarez-Muniz, J.
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Anchordoqui, L.
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Ferreira, P. R. Araujo
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Assis, P.
Avila, G.
Badescu, A. M.
Bakalova, A.
Balaceanu, A.
Barbato, F.
Barreira Luz, R. J.
Becker, K. H.
Bellido, J. A.
Berat, C.
Bertaina, M. E.
Bertou, X.
Biermann, P. L.
Bister, T.
Biteau, J.
Blanco, A.
Blazek, J.
Bleve, C.
Bohacova, M.
Boncioli, D.
Bonifazi, C.
Bonneau Arbeletche, L.
Borodai, N.
Botti, A. M.
Bracke, J.
Bretz, T.
Briechle, F. L.
Buchholz, P.
Bueno, A.
Buitink, S.
Buscemi, M.
Caballero-Mora, K. S.
Caccianiga, L.
Calcagni, L.
JOURNAL OF INSTRUMENTATION, 2020, 15 (09):
[9] Bayesian Deep Learning for Shower Parameter Reconstruction in Water Cherenkov Detectors
Bom, C. R.
Dias, L. O.
Conceicao, R.
Tome, B.
de Almeida, U. Barres
Moraes, A.
Pimenta, M.
Shellard, R.
Albuquerque, M. P.
37TH INTERNATIONAL COSMIC RAY CONFERENCE, ICRC2021, 2022,
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Allison, P.
Arneodo, F.
Bertou, X.
Busca, N.
Ghia, P. L.
Medina, C.
Navarra, G.
Nellen, L.
Ibarguen, H. Salazar
Ranchon, S.
Urban, M.
Villasenor, L.
Proceedings of the 29th International Cosmic Ray Conference Vol 8: HE 1.5, 2005, : 299 - 302

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