Deep learning techniques applied to the physics of extensive air showers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F19%3A00546579" target="_blank" >RIV/68378271:_____/19:00546579 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.astropartphys.2019.03.001" target="_blank" >https://doi.org/10.1016/j.astropartphys.2019.03.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.astropartphys.2019.03.001" target="_blank" >10.1016/j.astropartphys.2019.03.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning techniques applied to the physics of extensive air showers

Popis výsledku v původním jazyce

Deep neural networks are a powerful technique that have found ample applications in several branches of physics. In this work, we apply deep neural networks to a specific problem of cosmic ray physics: the estimation of the muon content of extensive air showers when measured at the ground. As a working case, we explore the performance of a deep neural network applied to large sets of simulated signals recorded for the water-Cherenkov detectors of the Surface Detector of the Pierre Auger Observatory. The inner structure of the neural network is optimized through the use of genetic algorithms. To obtain a prediction of the recorded muon signal in each individual detector, we train neural networks with a mixed sample of simulated events that contain light, intermediate and heavy nuclei. When true and predicted signals are compared at detector level, the primary values of the Pearson correlation coefficients are above 95%.

Název v anglickém jazyce

Deep learning techniques applied to the physics of extensive air showers

Popis výsledku anglicky

Deep neural networks are a powerful technique that have found ample applications in several branches of physics. In this work, we apply deep neural networks to a specific problem of cosmic ray physics: the estimation of the muon content of extensive air showers when measured at the ground. As a working case, we explore the performance of a deep neural network applied to large sets of simulated signals recorded for the water-Cherenkov detectors of the Surface Detector of the Pierre Auger Observatory. The inner structure of the neural network is optimized through the use of genetic algorithms. To obtain a prediction of the recorded muon signal in each individual detector, we train neural networks with a mixed sample of simulated events that contain light, intermediate and heavy nuclei. When true and predicted signals are compared at detector level, the primary values of the Pearson correlation coefficients are above 95%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

—

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Astroparticle Physics

ISSN

0927-6505

e-ISSN

1873-2852

Svazek periodika

111

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

12-22

Kód UT WoS článku

000470047300002

EID výsledku v databázi Scopus

2-s2.0-85063501854