Constraining the neutral hydrogen fraction during reionization: Cross-simulation inference using power spectrum and bispectrum
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 ($\bar{x}_{\mathrm{H\,I}}$) by training a neural network on 21cmFAST simulations and applying it to mock observations generated by the ReionYuga code. We first benchmark the framework in an idealized 21cmFAST-only setting before applying it to the more rigorous ReionYuga--21cmFAST cross-simulation case. Our analysis spans six redshifts and includes realistic SKA system noise and cosmic variance, calculated from 50 statistically independent realizations. In the same-code case, the bispectrum yields substantially tighter constraints, whereas in the cross-simulation case the improvement is moderate, with constraints tightened by $\sim 1.4\times$ relative to 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.