Event-by-event reconstruction of the shower max. with the Surface Detector of the Pierre Auger Observatory using deep learning

Discussion timeslot (ZOOM-Meeting): 16. July 2021 - 18:00
ZOOM-Meeting URL: https://icrc2021.desy.de/pf_access_abstracts
Corresponding Session: https://icrc2021-venue.desy.de/channel/Presenter-Forum-1-Evening-All-Categories/48
Abstract:

'The measurement of the mass composition of ultra-high energy cosmic rays constitutes one of thernbiggest challenges in astroparticle physics. Most detailed information on the compositionrncan be obtained from measurements of the depth of maximum of air showers, $X_mathrm{max}$, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights.rnrnUsing deep neural networks, it is now possible for the first time to perform an event-by-eventrnreconstruction of $X_mathrm{max}$ with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on $X_mathrm{max}$, and thus the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above $10^{19.5}$ eV.rnrnIn this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models and verify its functionality using Auger hybrid measurements. rnFinally, we quantify the expected systematic uncertainties and show that the method permits to determine the first two moments of the $X_mathrm{max}$ distributions up to the highest energies.'

Authors: Jonas Glombitza | For the Pierre Auger Collaboration
Collaboration: Auger

Indico-ID: 915
Proceeding URL: https://pos.sissa.it/395/359