A deep learning-based reconstruction of cosmic ray-induced air showers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F18%3A00547191" target="_blank" >RIV/68378271:_____/18:00547191 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.astropartphys.2017.10.006" target="_blank" >https://doi.org/10.1016/j.astropartphys.2017.10.006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.astropartphys.2017.10.006" target="_blank" >10.1016/j.astropartphys.2017.10.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A deep learning-based reconstruction of cosmic ray-induced air showers

Popis výsledku v původním jazyce

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.

Název v anglickém jazyce

A deep learning-based reconstruction of cosmic ray-induced air showers

Popis výsledku anglicky

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.

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

2018

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

97

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

46-53

Kód UT WoS článku

000423640700007

EID výsledku v databázi Scopus

2-s2.0-85034647911