Using Large Scale Structure to test Multifield Inflation

TL;DR

This paper forecasts how future large-scale structure surveys can constrain primordial non-Gaussianity parameters like $f_{NL}$, $g_{NL}$, and $ au_{NL}$ using galaxy bias, including stochastic effects, and discusses survey optimization.

Contribution

It provides new forecasting methods and fitting functions for constraining non-Gaussianity parameters from galaxy surveys, considering stochastic bias and halo mass information.

Findings

Next-generation surveys can constrain $f_{NL}$ to 6 without mass info.

Halo mass resolution improves constraints, achieving $\sigma(f_{NL})=1.5$.

Optimal weighting reduces sample variance, outperforming Planck constraints.

Abstract

Primordial non-Gaussianity of local type is known to produce a scale-dependent contribution to the galaxy bias. Several classes of multi-field inflationary models predict non-Gaussian bias which is stochastic, in the sense that dark matter and halos don't trace each other perfectly on large scales. In this work, we forecast the ability of next-generation Large Scale Structure surveys to constrain common types of primordial non-Gaussianity like $f_{NL}$, $g_{NL}$ and $\tau_{NL}$ using halo bias, including stochastic contributions. We provide fitting functions for statistical errors on these parameters which can be used for rapid forecasting or survey optimization. A next-generation survey with volume $V = 25 h^{-3}$Gpc$^3$, median redshift $z = 0.7$ and mean bias $b_g = 2.5$, can achieve $\sigma(f_{NL}) = 6$, $\sigma(g_{NL}) = 10^5$ and $\sigma(\tau_{NL}) = 10^3$ if no mass information is available. If halo masses are available, we show that optimally weighting the halo field in order to reduce sample variance can achieve $\sigma(f_{NL}) = 1.5$, $\sigma(g_{NL}) = 10^4$ and $\sigma(\tau_{NL}) = 100$ if halos with mass down to $M_{min} = 10^{11}$ $h^{-1} M_\odot $ are resolved, outperforming Planck by a factor of 4 on $f_{NL}$ and nearly an order of magnitude on $g_{NL}$ and $\tau_{NL}$. Finally, we study the effect of photometric redshift errors and discuss degeneracies between different non-Gaussian parameters, as well as the impact of marginalizing Gaussian bias and shot noise.