Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum

TL;DR

This paper introduces a new likelihood-free Bayesian inference method using density estimation for analyzing 21 cm power spectrum data from the epoch of reionization, outperforming traditional MCMC approaches.

Contribution

The paper presents a novel likelihood-free inference technique (DELFI) for reionization parameters, incorporating realistic observational effects and demonstrating improved accuracy over standard methods.

Findings

Accurately recovers posterior distributions for reionization parameters.

Outperforms standard MCMC in credible region estimation.

Provides a fast, minutes-level processing time after training.

Abstract

The first measurements of the 21 cm brightness temperature power spectrum from the epoch of reionization will very likely be achieved in the near future by radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). Standard MCMC analyses use an explicit likelihood approximation to infer the reionization parameters from the 21 cm power spectrum. In this paper, we present a new Bayesian inference of the reionization parameters where the likelihood is implicitly defined through forward simulations using density estimation likelihood-free inference (DELFI). Realistic effects including thermal noise and foreground avoidance are also applied to the mock observations from the HERA and SKA. We demonstrate that this method recovers accurate posterior distributions for the reionization parameters, and outperforms the standard MCMC analysis in terms of the location and size of credible parameter regions. With the minutes-level processing time once the network is trained, this technique is a promising approach for the scientific interpretation of future 21 cm power spectrum observation data. Our code 21cmDELFI-PS is publicly available at this link.