Radio-optical synergies at high redshift to constrain primordial non-Gaussianity

TL;DR

This paper demonstrates that combining radio and optical galaxy surveys using the multi-tracer technique can significantly improve constraints on primordial non-Gaussianity, achieving a precision of less than 1 on $f_{NL}$.

Contribution

The study introduces a multi-tracer approach combining high-redshift optical and radio surveys to enhance constraints on primordial non-Gaussianity beyond previous methods.

Findings

Multi-tracer technique reduces cosmic variance effects.

Combining surveys improves $f_{NL}$ constraints to $\sigma(f_{NL}) < 1$.

High-redshift surveys expand the accessible volume for analysis.

Abstract

We apply the multi-tracer technique to test the possibility of improved constraints on the amplitude of local primordial non-Gaussianity, $f_{\mathrm{NL}}$, in the cosmic large-scale structure. A precise measurement of $f_{\mathrm{NL}}$ is difficult because the effects of non-Gaussianity mostly arise on the largest scales, which are heavily affected by the low statistical sampling commonly referred to as cosmic variance. The multi-tracer approach suppresses cosmic variance and we implement it by combining the information from next-generation galaxy surveys in the optical/near-infrared band and neutral hydrogen (HI) intensity mapping surveys in the radio band. High-redshift surveys enhance the precision on $f_{\mathrm{NL}}$, due to the larger available volume, and HI intensity mapping surveys can naturally reach high redshifts. In order to extend the redshift coverage of a galaxy survey, we consider different emission-line galaxy populations, focusing on the H$\alpha$ line at low redshift and on oxygen lines at higher redshift. By doing so, we cover a wide redshift range $1 \lesssim z \lesssim 4$. To assess the capability of our approach, we implement a synthetic-data analysis by means of Markov chain Monte Carlo sampling of the (cosmological+nuisance) parameter posterior, to evaluate the constraints on $f_{\mathrm{NL}}$ obtained in different survey configurations. We find significant improvements from the multi-tracer technique: the full data set leads to a precision of $\sigma(f_{\mathrm{NL}}) < 1$.