Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH

Abstract

The redshifted 21-cm signal from the Cosmic Dawn and Epoch of Reionization carries invaluable information about the cosmology and astrophysics of the early Universe. Analyzing the data from a sky-averaged 21-cm signal experiment typically involves navigating through an intricate parameter space to accurately address various factors such as foregrounds, beam uncertainties, ionospheric distortions, and receiver noise for the search of the cosmological 21-cm signal. The traditional likelihood-based sampling methods for modeling these effects could become computationally demanding for such highly complex models, which makes it infeasible to include physically motivated 21-cm signal models in the analysis. Moreover, the inference with these traditional methods is driven by the assumed functional form of the likelihood function. This work demonstrates how Simulation-Based Inference through Truncated Marginal Neural Ratio Estimation (TMNRE) can naturally handle these issues at a significantly reduced computational cost than the likelihood-based methods. We estimate the posterior distribution on our model parameters with TMNRE for simulated mock observations, composed of beam-weighted foregrounds, physically motivated 21-cm signal, and radiometric noise. We find that maximizing the information content by simultaneously analyzing the data from multiple time slices and antennas significantly improves the parameter constraints and leads to a better exploration of the cosmological signal. We discuss the application of TMNRE for the current configuration of the REACH experiment and demonstrate how it can be utilized to explore potential avenues for REACH. The method presented here can be easily generalized for any sky-averaged 21-cm signal experiment.