  Machine learning (ML) techniques have been applied with tremendous success in
many areas of physics. In this work, we use ML to place bounds on the coupling
between photons and axion-like particles (ALPs). This coupling causes ALPs and
photons to interconvert in the presence of a background magnetic field. This
would lead to modulations in the spectra of point sources shining through the
magnetic fields of galaxy clusters. This effect has already been used to place
world-leading bounds on the ALP-photon coupling using conventional statistical
methods. We train ML classification algorithms on simulated spectra from the
Chandra X-ray telescope for a range of point sources and ALP-photon couplings.
We then use the response of these algorithms to the real Chandra spectra to
place bounds on ALP-photon interactions. We obtain bounds at a similar level to
those based on other techniques, but find improvements on an individual source
basis. We expect such search techniques to become increasingly important for
ALP searches with future telescopes that will offer substantially higher energy
resolution.
