Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A90247%2F24%3A00604760" target="_blank" >RIV/68378271:90247/24:00604760 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.nima.2023.168942" target="_blank" >https://doi.org/10.1016/j.nima.2023.168942</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.nima.2023.168942" target="_blank" >10.1016/j.nima.2023.168942</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

Popis výsledku v původním jazyce

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera images of such events to differentiate the signal from the background and to reconstruct the energy of the initial gamma ray. Pattern spectra provide a 2-dimensional histogram of the sizes and shapes of features comprising an image and they can be used as an input for a CNN to significantly reduce the computational power required to train it. In this work, we generate pattern spectra from simulated gamma-ray and proton images to train a CNN for signal-background separation and energy reconstruction for the Small-Sized Telescopes (SSTs) of the Cherenkov Telescope Array (CTA).

Název v anglickém jazyce

Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

Popis výsledku anglicky

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera images of such events to differentiate the signal from the background and to reconstruct the energy of the initial gamma ray. Pattern spectra provide a 2-dimensional histogram of the sizes and shapes of features comprising an image and they can be used as an input for a CNN to significantly reduce the computational power required to train it. In this work, we generate pattern spectra from simulated gamma-ray and proton images to train a CNN for signal-background separation and energy reconstruction for the Small-Sized Telescopes (SSTs) of the Cherenkov Telescope Array (CTA).

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

2024

Kód důvěrnosti údajů

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

Název periodika

Nuclear Instruments & Methods in Physics Research Section A

ISSN

0168-9002

e-ISSN

1872-9576

Svazek periodika

1059

Číslo periodika v rámci svazku

Feb

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

168942

Kód UT WoS článku

001128192000001

EID výsledku v databázi Scopus

2-s2.0-85178488947