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

THE PIERRE AUGER COLLABORATION (P. HANSEN PERTENECE A LA COLABORACION-ORDEN ALFABETICO)

JOURNAL OF INSTRUMENTATION

IOP PUBLISHING LTD

Lugar: Londres; Año: 2021

1748-0221

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 SurfaceDetector (SD). The SD samples the secondary particle content (mostly photons, electrons, positronsand muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eVup to more than 1020 eV. Measuring the independent contribution of the muon component to thetotal registered signal is crucial to enhance the capability of the Observatory to estimate the massof the cosmic rays on an event-by-event basis. However, with the current design of the SD, it isdifficult 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 onsimulations, in which the muon and the electromagnetic components of the traces are known. Weconclude this work showing the performance of this method on experimental data of the PierreAuger Observatory. We find that our predictions agree with the parameterizations obtained bythe AGASA collaboration to describe the lateral distributions of the electromagnetic and muoniccomponents of extensive air showers.Keywords: Analysis and statistical methods; Cherenkov