Heidelberg 2022 – scientific programme
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AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz
AKPIK 4: Deep Learning
AKPIK 4.6: Talk
Thursday, March 24, 2022, 17:30–17:45, AKPIK-H13
Measurement of the Mass Composition using the Surface Detector of the Pierre Auger Observatory and Deep Learning — Martin Erdmann, •Jonas Glombitza, and Niklas Langner for the Pierre Auger collaboration — III. Physics Institute A, RWTH Aachen
Measuring the mass composition of ultra-high energy cosmic rays (UHECRs) constitutes one of the biggest challenges in astroparticle physics. Nowadays, the most precise measurements can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of Fluorescence Detectors (FD), which can be operated only during clear and moonless nights.
With the advent of deep learning, it is now possible for the first time to perform an event-by-event reconstruction of Xmax using the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus the cosmic-ray composition. Since the SD features a duty cycle of nearly 100%, the gain in statistics is a factor of 15 for energies above 1019.5 eV compared to the FD.
This contribution introduces the neural network specifically 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. Finally, we quantify the expected systematic uncertainties and determine the UHECR mass composition using the first two moments of the Xmax distributions up to the highest energies.