BibTeX

@article{2509.20578v1,
Author        = {Pooja Bhattacharjee and Christopher Eckner and Gabrijela Zaharijas and Gert Kluge and Giacomo D'Amico},
Title         = {Probing the Parameter Space of Axion-Like Particles Using
  Simulation-Based Inference},
Eprint        = {2509.20578v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
Abstract      = {Axion-like particles (ALPs), hypothetical pseudoscalar particles that couple
to photons, are among the most actively investigated candidates for new physics
beyond the Standard Model. Their interaction with gamma rays in the presence of
astrophysical magnetic fields can leave characteristic, energy-dependent
modulations in observed spectra. Capturing such subtle features requires
precise statistical inference, but standard likelihood-based methods often fall
short when faced with complex models, large number of nuisance parameters and
limited analytical tractability. In this work, we investigate the application
of simulation-based inference (SBI), specifically Truncated Marginal Neural
Ratio Estimation (TMNRE), to constrain ALP parameters using simulated
observations from the upcoming Cherenkov Telescope Array Observatory (CTAO). We
model the gamma-ray emission from the active galactic nucleus NGC 1275,
accounting for photon-ALP mixing, extragalactic background light (EBL)
absorption, and the full CTAO instrument response. Leveraging the Swyft
framework, we infer posteriors for the ALP mass and coupling strength and
demonstrate its potential to extract meaningful constraints on ALPs from future
real gamma-ray data with CTAO.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.20578v1},
File          = {2509.20578v1.pdf}
}