What Can We Learn about Reionization Astrophysical Parameters Using Gaussian Process Regression?

Article Type

Research Article

Publication Title

Astrophysical Journal

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 Hubble Frontier Fields and early JWST data. From the reconstructed history of reionization, the global differential brightness temperature fluctuation during this epoch has been computed. We perform Markov Chain Monte Carlo (MCMC) analysis of the global 21 cm signal using the instrumental specifications of SARAS, in combination with Lyα ionization fraction data, Planck optical depth measurements, and UV luminosity data. Our analysis reveals that GPR can help infer the astrophysical parameters in a more 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 Square Kilometre Array, in combination with Planck and Lyα forest data, improves the bounds on the reionization astrophysical parameters by doing a joint MCMC analysis for the astrophysical parameters plus six cosmological parameters for the ΛCDM 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 and can be used for future analysis.

DOI

10.3847/1538-4357/ae113d

Publication Date

12-1-2025

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