Machine learning applications at the Pierre Auger Observatory

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00640972" target="_blank" >RIV/68378271:_____/25:00640972 - isvavai.cz</a>

Výsledek na webu

<a href="https://iopscience.iop.org/article/10.1088/1742-6596/3053/1/012011/pdf" target="_blank" >https://iopscience.iop.org/article/10.1088/1742-6596/3053/1/012011/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/3053/1/012011" target="_blank" >10.1088/1742-6596/3053/1/012011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning applications at the Pierre Auger Observatory

Popis výsledku v původním jazyce

The Pierre Auger Observatory is utilised to study the extensive air showers produced by ultra-high-energy cosmic rays. In this contribution, we provide an overview of the machine-learning techniques used by the Pierre Auger Collaboration to improve the understanding of data measured by the surface detector of the Observatory. The three methods presented use the spatial and temporal information contained in the signals measured by the surface detector stations. The first method demonstrates the application of deep learning techniques to reconstruct the energy of the cosmic ray. It has the potential to improve upon the standard technique by reducing the dependency of the energy on the primary mass. One of the primary objectives of the Observatory is to understand the evolution of the mass composition with energy. We can achieve this using observables such as the depth of the shower maximum and the number of muons reaching the ground. In the second method presented, long short-term memory and convolutional neural networks are employed to determine the depth of the shower maximum. The third work focuses on estimating both the depth of the shower maximum and the number of muons combining signals from the upgraded stations of the surface detector. Transformer networks are used for this purpose. Using simulations, we study the potential for an accurate reconstruction of the primary mass by combining the measurements of the shower maximum and the muon number.

Název v anglickém jazyce

Machine learning applications at the Pierre Auger Observatory

Popis výsledku anglicky

The Pierre Auger Observatory is utilised to study the extensive air showers produced by ultra-high-energy cosmic rays. In this contribution, we provide an overview of the machine-learning techniques used by the Pierre Auger Collaboration to improve the understanding of data measured by the surface detector of the Observatory. The three methods presented use the spatial and temporal information contained in the signals measured by the surface detector stations. The first method demonstrates the application of deep learning techniques to reconstruct the energy of the cosmic ray. It has the potential to improve upon the standard technique by reducing the dependency of the energy on the primary mass. One of the primary objectives of the Observatory is to understand the evolution of the mass composition with energy. We can achieve this using observables such as the depth of the shower maximum and the number of muons reaching the ground. In the second method presented, long short-term memory and convolutional neural networks are employed to determine the depth of the shower maximum. The third work focuses on estimating both the depth of the shower maximum and the number of muons combining signals from the upgraded stations of the surface detector. Transformer networks are used for this purpose. Using simulations, we study the potential for an accurate reconstruction of the primary mass by combining the measurements of the shower maximum and the muon number.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Journal of Physics: Conference Series

ISBN

—

ISSN

1742-6588

e-ISSN

—

Počet stran výsledku

6

Strana od-do

012011

Název nakladatele

IoP

Místo vydání

Bristol

Místo konání akce

Trapani

Datum konání akce

17. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001582869000011