Constraining the neutral hydrogen fraction during reionization: Cross-simulation inference using power spectrum and bispectrum

TL;DR

This study uses cross-simulation validation with neural networks to improve constraints on the neutral hydrogen fraction during reionization, highlighting the importance of modeling uncertainties and the moderate benefit of bispectrum data.

Contribution

It introduces a cross-simulation validation framework for 21cm signal analysis, demonstrating the impact of modeling uncertainties on parameter inference during reionization.

Findings

Bispectrum provides additional information but only modestly improves constraints.

Systematic discrepancies between models often exceed statistical uncertainties.

Modeling uncertainties are the main limitation for robust parameter inference.

Abstract

The redshifted 21-cm signal is a unique probe of the early universe, particularly the Epoch of Reionization (EoR). While the 21-cm power spectrum has been the primary statistic for parameter inference, it fails to capture the non-Gaussian information in the signal, motivating the use of higher-order statistics such as the bispectrum. We perform a rigorous cross-simulation validation to infer the mean neutral hydrogen fraction ($\bar{x}_{HI}$) by training a Bayesian neural network on 21cmFAST simulations and applying it to mock observations generated by ReionYuga code. Our analysis spans six redshifts and includes realistic SKA system noise and cosmic variance, calculated from 50 statistically independent realizations. We find that the bispectrum adds useful information, but the improvement is moderate, with constraints tightened by $\sim 1.4\times$ the power-spectrum only case. The cross-simulation analysis also identifies a persistent systematic discrepancy between inferred and true values that often exceeds the statistical uncertainties, implying that modeling uncertainty remains the dominant limitation. Our results, therefore, indicate that the highly stringent constraints obtained in same-code validation studies may be overly optimistic, and mitigating cross-model systematics is crucial for robust parameter inference in the SKA era.