Constraining large scale HI bias using redshifted 21-cm signal from the post-reionization epoch

TL;DR

This study investigates how well future 21-cm observations can constrain the large-scale HI bias and neutral hydrogen fraction post-reionization, using simulations and PCA analysis.

Contribution

It demonstrates the potential of PCA on simulated 21-cm data to constrain HI bias and neutral fraction at redshift 2.5, validating the linear bias model on large scales.

Findings

Simulated data constrains x_{HI} to about 4% accuracy.

Data cannot distinguish models with different k-dependences of bias within certain ranges.

Linear bias model is justified on large scales (0.2 < k < 2 Mpc^{-1}).

Abstract

In the absence of complex astrophysical processes that characterize the reionization era, the 21-cm emission from neutral hydrogen (HI) in the post-reionization epoch is believed to be an excellent tracer of the underlying dark matter distribution. Assuming a background cosmology, it is modelled through (i) a bias function b(k,z), which relates HI to the dark matter distribution and (ii) a mean neutral fraction (x_{HI}) which sets its amplitude. In this paper, we investigate the nature of large scale HI bias. The post-reionization HI is modelled using gravity only N-Body simulations and a suitable prescription for assigning gas to the dark matter halos. Using the simulated bias as the fiducial model for HI distribution at z\leq 4, we have generated a hypothetical data set for the 21-cm angular power spectrum (C_{l}) using a noise model based on parameters of an extended version of the GMRT. The binned C_{l} is assumed to be measured with SNR \gtrsim 4 in the range 400 \leq l \leq 8000 at a fiducial redshift z=2.5. We explore the possibility of constraining b(k) using the Principal Component Analysis (PCA) on this simulated data. Our analysis shows that in the range 0.2 < k < 2 Mpc^{-1}, the simulated data set cannot distinguish between models exhibiting different k dependences, provided 1 \lesssim b(k) \lesssim 2 which sets the 2-sigma limits. This justifies the use of linear bias model on large scales. The largely uncertain x_{HI} is treated as a free parameter resulting in degradation of the bias reconstruction. The given simulated data is found to constrain the fiducial x_{HI} with an accuracy of \sim 4% (2-sigma error). The method outlined here, could be successfully implemented on future observational data sets to constrain b(k,z) and x_{HI} and thereby enhance our understanding of the low redshift Universe.