Projected sensitivity of CTAO to axion-like particles from blazars with a machine learning approach

TL;DR

This paper evaluates CTAO's potential to detect axion-like particles from blazars using advanced simulations and introduces a machine learning approach to improve sensitivity and reduce uncertainties in ALP searches.

Contribution

It presents a novel machine learning method for ALP detection and provides projected sensitivity estimates for CTAO using realistic simulations of blazar observations.

Findings

CTAO can significantly improve current ALP constraints.

Machine learning classifiers can reduce systematic uncertainties.

Projected 2σ exclusion regions demonstrate enhanced sensitivity.

Abstract

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. In this work, we estimate the potential of the Cherenkov Telescope Array Observatory (CTAO) to constrain the ALP parameter space by simulating observations of two bright blazars, Mrk 501 and PKS 2155$-$304. We obtain projected $2\sigma$ exclusion regions, demonstrating that CTAO will be able to consistently improve present limits thanks to its greater energy resolution and point-source sensitivity with respect to present ground-based gamma-ray telescopes. In addition to the standard statistical technique based on the likelihood ratio test, we further demonstrate the application of a new method based on machine learning classifiers, which may help in reducing the effect of systematic model-dependent uncertainties in future ALP searches.