Deep-learning-based cosmic-ray mass reconstruction using the water-Cherenkov and scintillation detectors of AugerPrime

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00635946" target="_blank" >RIV/68378271:_____/24:00635946 - isvavai.cz</a>

Result on the web

<a href="https://pos.sissa.it/444/371/pdf" target="_blank" >https://pos.sissa.it/444/371/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22323/1.444.0371" target="_blank" >10.22323/1.444.0371</a>

Result language

angličtina

Original language name

Deep-learning-based cosmic-ray mass reconstruction using the water-Cherenkov and scintillation detectors of AugerPrime

Original language description

At the highest energies, cosmic rays can be detected only indirectly by the extensive air showers they create upon interaction with the Earth's atmosphere. While high-statistics measurements of the energy and arrival directions of cosmic rays can be performed with large surface detector arrays like the Pierre Auger Observatory, the determination of the cosmic-ray mass on an event-by-event basis is challenging. Meaningful physical observables in this regard include the depth of maximum of air-shower profiles, which is related to the mean free path of the cosmic ray in the atmosphere and the shower development, as well as the number of muons that rises with the number of nucleons in a cosmic-ray particle. In this contribution, we present an approach to determine both of these observables from combined measurements of water-Cherenkov detectors and scintillation detectors, which are part of the AugerPrime upgrade of the Observatory. To characterize the time-dependent signals of the two detectors both separately as well as in correlation to each other, we apply deep learning techniques. Transformer networks employing the attention mechanism are especially well-suited for this task. We present the utilized network concepts and apply them to simulations to determine the precision of the event-by-event mass reconstruction that can be achieved by the combined measurements of the depth of shower maximum and the number of muons.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10303 - Particles and field physics

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of Science

ISBN

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ISSN

1824-8039

e-ISSN

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Number of pages

13

Pages from-to

371

Publisher name

Sissa Medilab srl

Place of publication

Trieste

Event location

Nagoya

Event date

Jul 26, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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