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 10(17) eV up to more than 10(20) 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. (The Pierre Auger Collaboration)

Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks / A. Aab, P. Abreu, M. Aglietta, J.M. Albury, I. Allekotte, A. Almela, J. Alvarez-Muñiz, R. Alves Batista, G.A. Anastasi, L. Anchordoqui, B. Andrada, S. Andringa, C. Aramo, P.R. Araújo Ferreira, J.C. Arteaga Velázquez, H. Asorey, P. Assis, G. Avila, A.M. Badescu, A. Bakalova, A. Balaceanu, F. Barbato, R.J. Barreira Luz, K.H. Becker, J.A. Bellido, C. Berat, M.E. Bertaina, X. Bertou, P.L. Biermann, T. Bister, J. Biteau, J. Blazek, C. Bleve, M. Boháčová, D. Boncioli, C. Bonifazi, L. Bonneau Arbeletche, N. Borodai, A.M. Botti, J. Brack, T. Bretz, P.G. Brichetto Orchera, F.L. Briechle, P. Buchholz, A. Bueno, S. Buitink, M. Buscemi, K.S. Caballero-Mora, L. Caccianiga, F. Canfora, I. Caracas, J.M. Carceller, R. Caruso, A. Castellina, F. Catalani, G. Cataldi, L. Cazon, M. Cerda, J.A. Chinellato, K. Choi, J. Chudoba, L. Chytka, R.W. Clay, A.C. Cobos Cerutti, R. Colalillo, A. Coleman, M.R. Coluccia, R. Conceição, A. Condorelli, G. Consolati, F. Contreras, F. Convenga, D. Correia dos Santos, C.E. Covault, S. Dasso, K. Daumiller, B.R. Dawson, J.A. Day, R.M. de Almeida, J. de Jesús, S.J. de Jong, G. De Mauro, J.R.T. de Mello Neto, I. De Mitri, J. de Oliveira, D. de Oliveira Franco, F. de Palma, V. de Souza, E. De Vito, M. del Río, O. Deligny, A. Di Matteo, C. Dobrigkeit, J.C. D'Olivo, R.C. dos Anjos, M.T. Dova, J. Ebr, R. Engel, I. Epicoco, M. Erdmann, C.O. Escobar, A. Etchegoyen, H. Falcke, J. Farmer, G. Farrar, A.C. Fauth, N. Fazzini, F. Feldbusch, F. Fenu, B. Fick, J.M. Figueira, A. Filipčič, T. Fodran, M.M. Freire, T. Fujii, A. Fuster, C. Galea, C. Galelli, B. García, A.L. Garcia Vegas, H. Gemmeke, F. Gesualdi, A. Gherghel-Lascu, P.L. Ghia, U. Giaccari, M. Giammarchi, M. Giller, J. Glombitza, F. Gobbi, F. Gollan, G. Golup, M. Gómez Berisso, P.F. Gómez Vitale, J.P. Gongora, J.M. González, N. González, I. Goos, D. Góra, A. Gorgi, M. Gottowik, T.D. Grubb, F. Guarino, G.P. Guedes, E. Guido, S. Hahn, P. Hamal, M.R. Hampel, P. Hansen, D. Harari, V.M. Harvey, A. Haungs, T. Hebbeker, D. Heck, G.C. Hill, C. Hojvat, J.R. Hörandel, P. Horvath, M. Hrabovský, T. Huege, J. Hulsman, A. Insolia, P.G. Isar, P. Janecek, J.A. Johnsen, J. Jurysek, A. Kääpä, K.H. Kampert, B. Keilhauer, J. Kemp, H.O. Klages, M. Kleifges, J. Kleinfeller, M. Köpke, N. Kunka, B.L. Lago, R.G. Lang, N. Langner, M.A. Leigui de Oliveira, V. Lenok, A. Letessier-Selvon, I. Lhenry-Yvon, D. Lo Presti, L. Lopes, R. López, L. Lu, Q. Luce, A. Lucero, J.P. Lundquist, A. Machado Payeras, G. Mancarella, D. Mandat, B.C. Manning, J. Manshanden, P. Mantsch, S. Marafico, A.G. Mariazzi, I.C. Mariş, G. Marsella, D. Martello, H. Martinez, O. Martínez Bravo, M. Mastrodicasa, H.J. Mathes, J. Matthews, G. Matthiae, E. Mayotte, P.O. Mazur, G. Medina-Tanco, D. Melo, A. Menshikov, K.-. Merenda, S. Michal, M.I. Micheletti, L. Miramonti, S. Mollerach, F. Montanet, C. Morello, M. 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Zehrer, A. Zepeda. - In: JOURNAL OF INSTRUMENTATION. - ISSN 1748-0221. - 16:7(2021 Jul 12), pp. P07016.1-P07016.23. [10.1088/1748-0221/16/07/p07016]

Extraction of the muon signals recorded with the surface detector of the Pierre Auger Observatory using recurrent neural networks

L. Caccianiga;C. Galelli;L. Miramonti;V. Scherini;
2021

Abstract

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 10(17) eV up to more than 10(20) 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. (The Pierre Auger Collaboration)
Analysis and statistical methods; Cherenkov detectors; Large detector systems for particle and astroparticle physics; Pattern recognition; cluster finding; calibration and fitting methods;
Settore FIS/01 - Fisica Sperimentale
Settore FIS/04 - Fisica Nucleare e Subnucleare
Settore FIS/05 - Astronomia e Astrofisica
12-lug-2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1033151
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