An emulator-based forecasting on astrophysics and cosmology with 21 cm and density cross-correlations during EoR

Abstract

The 21 cm signal arising from fluctuations in the neutral hydrogen field, and its cross-correlation with other tracers of cosmic density, are promising probes of the high-redshift Universe. In this study, we assess the potential of the 21 cm power spectrum, along with its cross power spectrum with dark matter density and associated bias, to constrain both astrophysics during the reionization era and the underlying cosmology. Our methodology involves emulating these estimators using an Artificial Neural Network (ANN), enabling efficient exploration of the parameter space. Utilizing a photon-conserving semi-numerical reionization model, we construct emulators at a fixed redshift ($z = 7.0$) for $k$-modes relevant to upcoming telescopes such as SKA-Low. We generate $\sim7000$ training samples by varying both cosmological and astrophysical parameters along with initial conditions, achieving high accuracy when compared to true simulation outputs. While forecasting, the model involves five free parameters: three cosmological ($\Omega_m$, $h$, $\sigma_8$) and two astrophysical (ionizing efficiency, $\zeta$, and minimum halo mass, $M_{\mathrm{min}}$). Using a fiducial model at the mid-reionization stage, we create a mock dataset and perform forecasting with the trained emulators. Assuming a 5% observational uncertainty combined with emulator error, we find that the 21 cm and 21 cm-density cross power spectra can constrain the Hubble parameter ($h$) to better than 6% at a confidence interval of 95%, with tight constraints on the global neutral fraction ($Q_{\mathrm{HI}}$). The inclusion of bias information further improves constraints on $\sigma_8$ (< 10% at 95% confidence). Finally, robustness tests with two alternate ionization states and a variant with higher observational uncertainty show that the ionization fractions are still reliably recovered, even when cosmological constraints weaken.