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, Justin M. Albury, I. Allekotte, A. Almela, Jaime Álvarez-Muñiz, Rafael Alves Batista, Gioacchino Alex Anastasi, Luis A. Anchordoqui, Belén Andrada, S. Andringa, C. Aramo, Paulo Ricardo Araújo Ferreira, Juan Carlos Arteaga Velázquez, H. Asorey, P. Assis, G. Ávila, Alina Mihaela Badescu, Alena Bakalová, A. Balaceanu, Felicia Barbato, Ricardo Jorge Barreira Luz, K. Becker, Jose A. Bellido, Corinne Bérat, M. E. Bertaina, X. Bertou, Peter L. Biermann, Teresa Bister, Jonathan Biteau, Jiří Blažek, C. Bleve, M. Boháčová, Denise Boncioli, C. Bonifazi, Luan Bonneau Arbeletche, Nataliia Borodai, Ana Martina Botti, J. Brack, T. Bretz, P. Gabriel Brichetto Orchera, F. L. Briechle, P. Buchholz, A. Bueno, S. Buitink, Mario Buscemi, K. S. Caballero‐Mora, Lorenzo Caccianiga, Fabrizia Canfora, Ioana Caracas, J. M. Carceller, R. Caruso, A. Castellina, Fernando Catalani, G. Cataldi, Lorenzo Cazon, M. Cerda, J. A. Chinellato, K. Choi, J. Chudoba, L. Chytka, R. W. Clay, Agustín Cobos Cerutti, Roberta Colalillo, Alan Coleman, M. R. Coluccia, R. Conceição, Antonio Condorelli, Giovanni Consolati, F. Contreras, Fabio Convenga, Diego Correia dos Santos, C. E. Covault, S. Dasso, K. Daumiller, B. R. Dawson, J.A. Day, R. M. de Almeida, Joaquín de Jesús, S. J. de Jong, G. De Mauro, J.R.T. de Mello Neto, I. De Mitri, Jaime de Oliveira, Danelise de Oliveira Franco, F. de Palma, V. de Souza, Emanuele De Vito, M. del Río, O. Deligny, Armando di Matteo, C. Dobrigkeit, J. C. D’Olivo, R. C. dos Anjos, M. T. Dova, Jan Ebr, R. Engel, Italo Epicoco, M. Erdmann
Abstract
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.