📝 SummarXiv

Abstract

The Lyman-alpha (Ly$\alpha$) forest at $z \sim 5$ offers a primary probe to constrain the history of the Epoch of Reionization (EoR), retaining thermal and ionization signatures imprinted by the reionization process. In this work, we present a new inference framework based on JAX that combines forward-modeled Ly$\alpha$ forest observables with differentiable neural emulators and Hamiltonian Monte Carlo (HMC). We construct a dataset of 501 low-resolution simulations generated with user-defined reionization histories and compute a set of 1D Ly$\alpha$ power spectra and model-dependent covariance matrices. We then train two independent neural emulators that achieve sub-percent errors across relevant scales and combine them with HMC to efficiently perform parameter estimation. We validate this framework by applying it to a suite of mock observations, demonstrating that the true parameters are reliably recovered. While this work is limited by the low resolution of the simulations used, our results highlight the potential of this method for inferring the reionization history from high-redshift Ly$\alpha$ forest measurements. Future improvements in our reionization models will further enhance its ability to extract constraints from observational datasets.