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

Journal of Instrumentation, vol. 16, pp. P07019

Abstract

Abstract 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/cm2 at energies above 2 × 1019 eV.

Authors 6

1. P. Abreu

   University of Lisbon · LIP - Laboratory of Instrumentation and Experimental Particle Physics · Instituto Superior Técnico

   Affiliation as printed

   Laboratório de Instrumentação e Física Experimental de Partículas — LIP and Instituto Superior Técnico — IST, Universidade de Lisboa — UL, Lisboa, Portugal

2. X. Bertou

   Consejo Nacional de Investigaciones Científicas y Técnicas · Balseiro Institute · Bariloche Atomic Centre

   Affiliation as printed

   Centro Atómico Bariloche and Instituto Balseiro (CNEA-UNCuyo-CONICET), San Carlos de Bariloche, Argentina

3. C. Bonifazi

   Universidade Federal do Rio de Janeiro

   Affiliation as printed

   Universidade Federal do Rio de Janeiro, Instituto de Física, Rio de Janeiro, RJ, Brazil

4. Lorenzo Caccianiga

   University of Milan · Istituto Nazionale di Fisica Nucleare, Sezione di Milano

   Affiliation as printed

   INFN, Sezione di Milano, Milano, Italy

   Università di Milano, Dipartimento di Fisica, Milano, Italy

5. Olivier Deligny

   Centre National de la Recherche Scientifique · Université Paris-Saclay · Institut National de Physique Nucléaire et de Physique des Particules · Laboratoire de Physique des 2 Infinis Irène Joliot-Curie

   Affiliation as printed

   CNRS/IN2P3, IJCLab, Université Paris-Saclay, Orsay, France

6. Robert Engel

   Karlsruhe Institute of Technology

   Affiliation as printed

   Karlsruhe Institute of Technology (KIT), Institute for Astroparticle Physics, Karlsruhe, Germany

   Karlsruhe Institute of Technology (KIT), Institute for Experimental Particle Physics, Karlsruhe, Germany