Ezequiel Rodriguez (Avtor), Andrej Filipčič (Avtor), J. P. Lundquist (Avtor), S. U. Shivashankara (Avtor), Samo Stanič (Avtor), Serguei Vorobiov (Avtor), Danilo Zavrtanik (Avtor), Marko Zavrtanik (Avtor)

Povzetek

Ultra-high-energy photons (E ≥ 10[sup]17 eV) are expected as by-products of interactions between ultra-high-energy cosmic rays (UHECRs) and background radiation fields or galactic matter, as well as from decay of super-heavy dark matter. Despite these various production mechanisms, the diffuse photon flux is too low for direct detection. Consequently, photon searches at UHE must rely on large ground-based detector arrays. In this contribution, we present a method for photon-hadron discrimination based on deep learning algorithms applied to detector simulations within the context of the Pierre Auger Observatory. Our method correlates information from the Surface Detector (SD), sensitive to air-shower particles arriving to the ground, and the Underground Muon Detector (UMD), sensitive to muons with energies above ∼ 1 GeV. We chose graph neural networks (GNNs) for their effectiveness in handling the discrimination task, allowing for an easy and flexible correlation of information from the SD and UMD. This approach is particularly suitable for handling the irregular structures found in SD and UMD configurations, where stations may be missing due to technical issues. Using simulations, the performance indicates that the method has strong potential for identifying photons, suffering at most 10[sup]−4 background contamination at 0.5 signal efficiency. Future studies will delve into how much that background contamination can be diminished.

Ključne besede

ultra-high-energy cosmic rays;Pierre Auger Observatory;UHE photons;extensive air showers;AugerPrime upgrade;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija: UNG - Univerza v Novi Gorici
UDK: 52
COBISS: 237705731 Povezava se bo odprla v novem oknu
ISSN: 1824-8039
Št. ogledov: 61
Št. prenosov: 0
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Strani: 8 str.
DOI: 10.22323/1.484.0111
ID: 26465151