Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys

Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of next generation galaxy surveys. The signal will permit us to determine primordial-physics processes and constrain models of cosmic inflation. While traditional approaches use a limited set of statistical summaries of the galaxy distribution to constrain primordial non-Gaussianity, we present a field-level approach by Bayesian forward modelling the entire three-dimensional galaxy survey. Since our method includes the entire cosmic field in the analysis, it can naturally and fully self-consistently exploit all available information in the large-scale structure, to extract information on the local non-Gaussianity parameter, f(nl). Examples include higher order statistics through correlation functions, peculiar velocity fields through redshift-space distortions, and scale-dependent galaxy bias. To illustrate the feasibility of field-level primordial non-Gaussianity inference, we present our approach using a first-order Lagrangian perturbation theory model, approximating structure growth at sufficiently large scales. We demonstrate the performance of our approach through various tests with self-consistent mock galaxy data emulating relev ant features of the SDSS-III/BOSS-like survey, and additional tests with a Sta ge IV mock data set. These tests reveal that the method infers unbiased values of f(nl )by accurately handling survey geometries, noise, and unknown galaxy biases. We demonstrate that our method can achieve constraints of sigma fnl asymptotic to 8 . 78 for SDSS-III/BOSS-like data, indicating potential improvements of a factor similar to 2.5 over current published constraints. We perform resolution studies on scales larger than similar to 16h(-1) Mpc showing the promise of significant constraints with next-generation surveys. Furthermore, the results demonstrate that our method can consistently marginalize all nuisance parameters of the data model. The method further provides an inference of the three-dimensional primordial density field, providing opportunities to explore additional signatures of primordial physics. This first demonstration of a field-level inference pipeline demonstrates a promising complementary path forward for analysing next-generation surveys.

Automated summary

Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of next generation galaxy surveys. A field-level approach by Bayesian forward modelling the entire three-dimensional galaxy survey is presented to extract information on primordial non-Gaussianity. The method demonstrates potential improvements of a factor similar to 2.5 over current published constraints and provides a promising complementary path for analyzing next-generation surveys.