Simulated multi-tracer analyses with HI intensity mapping

TL;DR

This paper investigates the potential of multi-tracer analyses combining HI intensity mapping and photometric galaxy surveys to improve large-scale structure measurements, accounting for foreground effects and noise.

Contribution

It demonstrates how multi-tracer ratios can measure bias on large scales and explores strategies to mitigate foreground removal impacts, enhancing cosmological parameter estimation.

Findings

Multi-tracer estimators improve sensitivity by 2-4 times over cosmic variance-limited measurements.

Cross-correlation estimators are robust against foreground-induced biases in auto-correlations.

Foreground removal reduces sensitivity but remains better than single-tracer cosmic variance limits.

Abstract

We use full sky simulations, including the effects of foreground contamination and removal, to explore multi-tracer synergies between a SKA-like 21cm intensity mapping survey and a LSST-like photometric galaxy redshift survey. In particular we study ratios of auto and cross-correlations between the two tracers as estimators of the ratio of their biases, a quantity that should benefit considerably from the cosmic variance cancellation of the multi-tracer approach. We show how well we should be able to measure the bias ratio on very large scales (down to $\ell \sim 3$), which is crucial to measure primordial non-Gaussianity and general relativistic effects on large scale structure. We find that, in the absence of foregrounds but with realistic noise levels of such surveys, the multi-tracer estimators are able to improve on the sensitivity of a cosmic-variance contaminated measurement by a factor of $2-4$. When foregrounds are included, estimators using the 21cm auto-correlation become biased. However, we show that cross-correlation estimators are immune to this and do not incur in any significant penalty in terms of sensitivity from discarding the auto-correlation data. However, the loss of long-wavelength radial modes caused by foreground removal in combination with the low redshift resolution of photometric surveys, reduces the sensitivity of the multi-tracer estimator, albeit still better than the cosmic variance contaminated scenario even in the noise free case. Finally we explore different alternative avenues to avoid this problem.