Physics Beyond the Standard Model with Future X-ray Observatories: Projected Constraints on Very-Light Axion-Like Particles with $Athena$ and $AXIS$

TL;DR

This paper evaluates the potential of future X-ray observatories, Athena and AXIS, to constrain very-light axion-like particles through observations of AGN in galaxy clusters, demonstrating significant improvements over current limits and the role of machine learning in mitigating calibration uncertainties.

Contribution

It introduces a novel approach using neural networks to recover ALP constraints from X-ray data despite instrumental calibration uncertainties.

Findings

Athena can exclude photon-ALP couplings > 6.3 x 10^-14 GeV^-1 with 200 ks exposure.

Machine learning techniques can restore sensitivity lost due to calibration uncertainties.

AXIS observations can improve constraints on ALPs by a factor of 3.

Abstract

Axion-Like Particles (ALPs) are well-motivated extensions of the Standard Model of Particle Physics and a generic prediction of some string theories. X-ray observations of bright Active Galactic Nuclei (AGN) hosted by rich clusters of galaxies are excellent probes of very-light ALPs, with masses $\mathrm{log}(m_\mathrm{a}/\mathrm{eV}) < -12.0$. We evaluate the potential of future X-ray observatories, particularly $Athena$ and the proposed $AXIS$, to constrain ALPs via observations of cluster-hosted AGN, taking NGC 1275 in the Perseus cluster as our exemplar. Assuming perfect knowledge of instrument calibration, we show that a modest exposure (200-ks) of NGC 1275 by $Athena$ permits us to exclude all photon-ALP couplings $g_\mathrm{a\gamma} > 6.3 \times 10^{-14} \ {\mathrm{GeV}}^{-1}$ at the 95% level, as previously shown by $Conlon \ et \ al. \ (2018)$, representing a factor of 10 improvement over current limits. We then proceed to assess the impact of realistic calibration uncertainties on the $Athena$ projection by applying a standard $Cash$ likelihood procedure, showing the projected constraints on $g_\mathrm{a\gamma}$ weaken by a factor of 10 (back to the current most sensitive constraints). However, we show how the use of a deep neural network can disentangle the energy-dependent features induced by instrumental miscalibration and those induced by photon-ALP mixing, allowing us to recover most of the sensitivity to the ALP physics. In our explicit demonstration, the machine learning applied allows us to exclude $g_\mathrm{a\gamma} > 2.0 \times 10^{-13} \ {\mathrm{GeV}}^{-1}$, complementing the projected constraints of next-generation ALP dark matter birefringent cavity searches for very-light ALPs. Finally, we show that a 200-ks $AXIS$/on-axis observation of NGC 1275 will tighten the current best constraints on very-light ALPs by a factor of 3.