Physical Sciences and Mathematics Commons^™

Deep-Learning Based Reconstruction Of The Shower Maximum Xmax Using The Water-Cherenkov Detectors Of The Pierre Auger Observatory, A. Aab, P. Abreu, M. Aglietta, J. M. Albury, I. Allekotte, A. Almela, B. Fick, D. F. Nitz, A. Puyleart, Et. Al.

Michigan Tech Publications

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax 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 Xmax 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 Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov …