What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?

TL;DR

This paper employs Gaussian Process Regression to reconstruct the reionization history, infer astrophysical parameters, and analyze the 21-cm signal, demonstrating the method's robustness and potential for future cosmological studies.

Contribution

It introduces a model-agnostic GPR approach to infer reionization parameters and combines multiple datasets to improve constraints on astrophysical and cosmological parameters.

Findings

GPR effectively reconstructs the reionization history.

Joint analysis with future SKA data tightens parameter bounds.

GPR-based inferences are robust and reliable for future research.

Abstract

Reionization is one of the least understood processes in the evolution history of the Universe, mostly because of the numerous astrophysical processes occurring simultaneously about which we do not have a very clear idea so far. In this article, we use the Gaussian Process Regression (GPR) method to learn the reionization history and infer the astrophysical parameters. We reconstruct the UV luminosity density function using the HFF and early JWST data. From the reconstructed history of reionization, the global differential brightness temperature fluctuation during this epoch has been computed. We perform MCMC analysis of the global 21-cm signal using the instrumental specifications of SARAS, in combination with Lyman-$\alpha$ ionization fraction data, Planck optical depth measurements and UV luminosity data. Our analysis reveals that GPR can help infer the astrophysical parameters in a model-agnostic way than conventional methods. Additionally, we analyze the 21-cm power spectrum using the reconstructed history of reionization and demonstrate how the future 21-cm mission SKA, in combination with Planck and Lyman-$\alpha$ forest data, improves the bounds on the reionization astrophysical parameters by doing a joint MCMC analysis for the astrophysical parameters plus 6 cosmological parameters for $\Lambda$CDM model. The results make the GPR-based reconstruction technique a robust learning process and the inferences on the astrophysical parameters obtained therefrom are quite reliable that can be used for future analysis.