Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory

Understand

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays.

• Direct measurements of $X_{\mathrm{max}}$ 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 $X_{\mathrm{max}}$ 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 $X_{\mathrm{max}}$.