Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F21%3A00539716" target="_blank" >RIV/68378271:_____/21:00539716 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.astropartphys.2020.102527" target="_blank" >https://doi.org/10.1016/j.astropartphys.2020.102527</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.astropartphys.2020.102527" target="_blank" >10.1016/j.astropartphys.2020.102527</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks

Popis výsledku v původním jazyce

We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.

Název v anglickém jazyce

Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks

Popis výsledku anglicky

We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.

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

2021

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

—

Svazek periodika

126

Číslo periodika v rámci svazku

Mar

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

000600571200006

EID výsledku v databázi Scopus

2-s2.0-85096862548