A machine learning approach to axion-like particle searches in CTAO observations of blazars

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00648261" target="_blank" >RIV/68378271:_____/25:00648261 - isvavai.cz</a>

Výsledek na webu

<a href="https://pos.sissa.it/501/834/pdf" target="_blank" >https://pos.sissa.it/501/834/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22323/1.501.0834" target="_blank" >10.22323/1.501.0834</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A machine learning approach to axion-like particle searches in CTAO observations of blazars

Popis výsledku v původním jazyce

Axion-like particles (ALPs) are a common prediction of several extensions of the Standard Model of particle physics and could be detected through their coupling to photons, which enables ALP-photon conversions in external magnetic fields. This conversion could lead to two distinct signatures in gamma-ray spectra of blazars: a superimposition of energy-dependent 'wiggles' on the spectral shape, and a hardening at high (multi-TeV) energies, due to the ALP beam eluding absorption by the extragalactic background light (EBL). The enhanced energy resolution of the Cherenkov Telescope Array Observatory (CTAO) with respect to present ground-based gamma-ray telescopes makes it an ideal instrument to probe such phenomena. In this contribution, we explore a different approach based on the use of machine learning (ML) classifiers and compare it to the standard method. Our preliminary results suggest that both techniques yield consistent results, with the ML-based method offering comparable or even slightly broader coverage, potentially extending the CTAO sensitivity beyond existing constraints.n

Název v anglickém jazyce

A machine learning approach to axion-like particle searches in CTAO observations of blazars

Popis výsledku anglicky

Axion-like particles (ALPs) are a common prediction of several extensions of the Standard Model of particle physics and could be detected through their coupling to photons, which enables ALP-photon conversions in external magnetic fields. This conversion could lead to two distinct signatures in gamma-ray spectra of blazars: a superimposition of energy-dependent 'wiggles' on the spectral shape, and a hardening at high (multi-TeV) energies, due to the ALP beam eluding absorption by the extragalactic background light (EBL). The enhanced energy resolution of the Cherenkov Telescope Array Observatory (CTAO) with respect to present ground-based gamma-ray telescopes makes it an ideal instrument to probe such phenomena. In this contribution, we explore a different approach based on the use of machine learning (ML) classifiers and compare it to the standard method. Our preliminary results suggest that both techniques yield consistent results, with the ML-based method offering comparable or even slightly broader coverage, potentially extending the CTAO sensitivity beyond existing constraints.n

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

Proceedings of Science

ISBN

—

ISSN

1824-8039

e-ISSN

—

Počet stran výsledku

7

Strana od-do

834

Název nakladatele

Sissa Medilab srl

Místo vydání

Trieste

Místo konání akce

Ženeva

Datum konání akce

15. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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