Probing delayed-end reionization histories with the 21cm-LAE cross-power spectrum

TL;DR

This paper models the 21cm-LAE cross-power spectrum during reionization to assess how future surveys can constrain the timing and progression of reionization, especially its delayed end, using simulations consistent with current constraints.

Contribution

It introduces high-dynamic-range simulations of the IGM to predict the 21cm-LAE cross-power spectrum for various reionization histories and evaluates the sensitivity of upcoming surveys to detect this signal.

Findings

Future surveys can detect delayed reionization signatures.

Subaru-SKA can measure reionization end at z=6.6 with high confidence.

Expanding survey area improves signal detection significantly.

Abstract

We model the 21-cm signal and LAE population evolution during the epoch of reionization in order to predict the 21cm-LAE cross-power spectrum. We employ high-dynamic-range simulations of the IGM to create models that are consistent with constraints from the CMB, Lyman-$\alpha$ forest and LAE population statistics. Using these models we consider the evolution of the cross-power spectrum for a selection of realistic reionization histories and predict the sensitivity of current and upcoming surveys to measuring this signal. We find that the imprint of a delayed-end to reionization can be observed by future surveys, and that strong constraints can be placed on the progression of reionization as late as $z=5.7$ using a Subaru-SKA survey. We make predictions for the signal-to-noise ratios achievable by combinations of Subaru/PFS with the MWA, LOFAR, HERA and SKA interferometers for an integration time of 1000 hours. We find that a Subaru-SKA survey could measure the cross-power spectrum for a late reionization at $z=6.6$ with a total signal-to-noise greater than 5, making it possible to constrain both the timing and bubble size at the end of reionization. Furthermore, we find that expanding the current Subaru/PFS survey area and depth by a factor of three would double the total signal-to-noise.