The cross-correlation between 21 cm intensity mapping maps and the Lyα forest in the post-reionization era

We investigate the cross-correlation signal between 21cm intensity mapping maps and the Lyα forest in the fully non-linear regime using state-of-the-art hydrodynamic simulations. The cross-correlation signal between the Lyα forest and 21cm maps can provide a coherent and comprehensive picture of the neutral hydrogen (HI) content of our Universe in the post-reionization era, probing both its mass content and volume distribution. We compute the auto-power spectra of both fields together with their cross-power spectrum at z=2.4 and find that on large scales the fields are completely anti-correlated. This anti-correlation arises because regions with high (low) 21cm emission, such as those with a large (low) concentration of damped Lyα systems, will show up as regions with low (high) transmitted flux. We find that on scales smaller than k'0.2h Mpc−1 the cross-correlation coefficient departs from−1, at a scale where non-linearities show up. We use the anisotropy of the power spectra in redshift-space to determine the values of the bias and of the redshift-space distortion parameters of both fields. We find that the errors on the value of the cosmological and astrophysical parameters could decrease by 30% when adding data from the cross-power spectrum, in a conservative analysis. Our results point out that linear theory is capable of reproducing the shape and amplitude of the cross-power up to rather non-linear scales. Finally, we find that the 21cm-Lyα cross-power spectrum can be detected by combining data from a BOSS-like survey together with 21cm intensity mapping observations by SKA1-MID with a S/N ratio higher than 3 in k∈[0.06,1]h Mpc−1. We emphasize that while the shape and amplitude of the 21cm auto-power spectrum can be severely affected by residual foreground contamination, cross-power spectra will be less sensitive to that and therefore can be used to identify systematics in the 21cm maps. © Copyright 2017 IOP Publishing