Inference of the Mass Composition of Cosmic Rays with energies from $10^{18.5}$ to $10^{20}$ eV using the Pierre Auger Observatory and Deep Learning

Abstract

We present measurements of the atmospheric depth of the shower maximum $X_{\max }$, 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 $X_{\max }$ distributions up to energies of 100 EeV ($10^{20}$ 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 $X_{\max }$ with the logarithm of energy features three breaks at $6.5\pm 0.6(\mathrm{stat})\pm 1(\mathrm{sys})$ EeV, $11\pm 2(\mathrm{stat})\pm 1(\mathrm{sys})$ EeV, and $31\pm 5(\mathrm{stat})\pm 3(\mathrm{sys})$ EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured $X_{\max }$ distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV.

Version accepted for publication in Phys. Rev. Lett., 9 pages, 3 figures, 1 table