Inference of the Mass Composition of Cosmic Rays with energies from $\mathbf{10^{18.5}}$ to $\mathbf{10^{20}}$ eV using the Pierre Auger Observatory and Deep Learning
The Pierre Auger Collaboration: A. Abdul Halim, P. Abreu, M. Aglietta,, I. Allekotte, K. Almeida Cheminant, A. Almela, R. Aloisio, J., Alvarez-Mu\~niz, J. Ammerman Yebra, G.A. Anastasi, L. Anchordoqui, B., Andrada, L. Andrade Dourado, S. Andringa, L. Apollonio, C. Aramo, P.R.

TL;DR
This study uses deep learning to measure the atmospheric depth of cosmic ray showers on an event-by-event basis, extending data to ultra-high energies and revealing a trend towards heavier, purer cosmic ray composition at the highest energies.
Contribution
It introduces a deep learning method to infer $X_{max}$ for individual events, enabling measurements at energies up to 100 EeV, surpassing previous fluorescence detector capabilities.
Findings
Identifies three breaks in the $X_{max}$ evolution at specific energies.
Finds the cosmic ray composition becomes heavier and more uniform at energies above 50 EeV.
Provides evidence against a dominant light nuclei component at ultra-high energies.
Abstract
We present measurements of the atmospheric depth of the shower maximum , inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the distributions up to energies of 100 EeV ( eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average with the logarithm of energy features three breaks at EeV, EeV, and EeV, in the vicinity to the three prominent features (ankle, instep,…
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