Using large scale structure to measurefNL,gNLandτNL

Abstract

Primordial non-Gaussianity of local type is known to produce a scale-dependent contribution to the galaxy bias. Several classes of multifield inflationary models predict non-Gaussian bias which is stochastic, in the sense that dark matter and halos do not 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 ${f}_{\mathrm{NL}}$, ${g}_{\mathrm{NL}}$ and ${\ensuremath{\tau}}_{\mathrm{NL}}$ 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 $V=25{h}^{\ensuremath{-}3}\text{ }\text{ }{\mathrm{Gpc}}^{3}$, median redshift $z=0.7$ and mean bias ${b}_{g}=2.5$ can achieve $\ensuremath{\sigma}({f}_{\mathrm{NL}})=6$, $\ensuremath{\sigma}({g}_{\mathrm{NL}})={10}^{5}$ and $\ensuremath{\sigma}({\ensuremath{\tau}}_{\mathrm{NL}})={10}^{3}$ 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 $\ensuremath{\sigma}({f}_{\mathrm{NL}})=1.5$, $\ensuremath{\sigma}({g}_{\mathrm{NL}})={10}^{4}$ and $\ensuremath{\sigma}({\ensuremath{\tau}}_{\mathrm{NL}})=100$ if halos with mass down to ${M}_{\mathrm{min}}={10}^{11}{h}^{\ensuremath{-}1}{M}_{\ensuremath{\bigodot}}$ are resolved, outperforming Planck by a factor of 4 on ${f}_{\mathrm{NL}}$ and nearly an order of magnitude on ${g}_{\mathrm{NL}}$ and ${\ensuremath{\tau}}_{\mathrm{NL}}$. 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.