Non-Gaussianity Constraints using Future Radio Continuum Surveys and the Multi-Tracer Technique

TL;DR

This paper forecasts how future radio continuum surveys, using the multi-tracer technique and realistic observational parameters, can significantly improve constraints on primordial non-Gaussianity, rivaling current best limits.

Contribution

It introduces more realistic forecasts for $f_{NL}$ constraints by incorporating observational bias, halo mass estimates, and updated simulations in the multi-tracer analysis.

Findings

1-σ error on $f_{NL}$ ranges from 4.07 to 6.58 in realistic scenarios.

Multi-tracer method effectively reduces cosmic variance effects.

Forecasts are comparable to the tightest current constraints.

Abstract

Tighter constraints on measurements of primordial non-Gaussianity will allow the differentiation of inflationary scenarios. The cosmic microwave background bispectrum-the standard method of measuring the local non-Gaussianity-is limited by cosmic variance. Therefore, it is sensible to investigate measurements of non-Gaussianity using the large-scale structure. This can be done by investigating the effects of non-Gaussianity on the power spectrum on large scales. In this study we forecast the constraints on the local primordial non-Gaussianity parameter $f_{\rm NL}$ that can be obtained with future radio surveys. We utilize the multi-tracer method which reduces the effect of cosmic variance and takes advantage of the multiple radio galaxy populations which are differently biased tracers of the same underlying dark matter distribution. Improvements on previous work include the use of observational bias and halo mass estimates, updated simulations and realistic photometric redshift expectations, thus producing more realistic forecasts. Combinations of SKA simulations and radio observations were used as well as different redshift ranges and redshift bin sizes. It was found that in the most realistic case the 1 - $\sigma$ error on $f_{\rm NL}$ falls within the range 4.07 and 6.58, rivalling the tightest constraints currently available.