Ponente
Descripción
Blazars are a class of active galactic nuclei, supermassive black holes located at the centres of distant galaxies characterised by strong emission across the entire electromagnetic spectrum, from radio waves to gamma rays. Their relativistic jets, closely aligned to the line of sight from Earth, are a rich and complex environment, characterised by the presence of strong magnetic fields over parsec-scale lengths. Owing to their cosmological distance from Earth, these sources serve as ideal targets to probe non-standard gamma-ray propagation. In particular, axion-like particles (ALPs) could be detected through their coupling to photons, which enables ALP-photon conversions in external magnetic fields, leading to distinct signatures in the blazars’ gamma-ray spectra. The Cherenkov Telescope Array Observatory (CTAO), with its enhanced energy resolution and point-source sensitivity with respect to present ground-based gamma-ray telescopes, will be a next-generation instrument very well fit to probe such features. In this contribution, we explore an approach based on the use of machine learning (ML) classifiers and compare it to the standard method of likelihood-ratio test, previously applied in CTAO sensitivity studies for ALP signatures. Our preliminary $2\sigma$ exclusion regions on the ALP parameter space suggest that both techniques yield consistent results, with the ML-based method offering broader coverage and potentially extending the CTAO sensitivity beyond existing constraints.
