Updated Bounds on Axion-Like Particles from X-ray Observations

TL;DR

This paper improves constraints on axion-like particles by applying machine learning and Bayesian methods to X-ray data from galaxy clusters, incorporating realistic magnetic field models, and achieving bounds comparable to the best existing limits.

Contribution

It introduces the use of approximate Bayesian computation and compares ML techniques with traditional analysis for ALP searches, utilizing advanced 3D magnetic field simulations.

Findings

Constraints on ALP-photon coupling are at $g_{a ext{γγ}} \\lesssim 0.6 \\times 10^{-12}$ GeV$^{-1}$

Machine learning methods perform well in ALP searches, matching traditional $\\chi^2$ analysis.

Realistic 3D magnetic field models improve the robustness of the constraints.

Abstract

In this work we revisit five different point sources within or behind galaxy clusters in order to constrain the coupling constant between axion-like particles (ALPs) and photons. We use three distinct machine learning (ML) techniques and compare our results with a standard $\chi^2$ analysis. For the first time we apply approximate Bayesian computation to searches for ALPs and find consistently good performance across ML classifiers. Further, we apply more realistic 3D magnetic field simulations of galaxy clusters and compare our results with previously used 1D simulations. We find constraints on the ALP-photon coupling at the level of state-of-the-art bounds with $g_{a\gamma\gamma} \lesssim 0.6 \times 10^{-12}$ GeV${}^{-1}$, hence improving on previous constraints obtained from the same observations.