Primordial Non-Gaussianity and the Field-Level Cramer-Rao Bound

TL;DR

This paper investigates the ultimate limits of detecting primordial non-Gaussianity in galaxy maps using the field-level Cramer-Rao bound, highlighting the potential and limitations of multi-tracer methods and future survey forecasts.

Contribution

It introduces the use of the field-level Cramer-Rao bound to assess the maximum extractable primordial non-Gaussianity information from galaxy surveys.

Findings

Multi-tracer scale-dependent bias can surpass conservative higher-point analyses for local non-Gaussianity.

Including all halo modes, multi-tracer analysis approaches the optimal constraint but does not surpass it.

Future surveys' sensitivity to equilateral and local non-Gaussianity depends heavily on priors and modeling assumptions.

Abstract

Primordial non-Gaussianity is one of the most powerful probes of the inflationary epoch. The particle spectrum relevant to inflation, including masses and spins, is encoded in the precise form of statistical correlations of the adiabatic modes. Yet, in the presence of nonlinear structure formation, the optimal approach to measuring these signals remains unclear. Accurate modeling becomes crucial as late-time non-Gaussianty can become degenerate with primordial physics. Moreover, scale-dependent bias shows that information can move from non-Gaussian initial conditions to the amplitude of the Gaussian fluctuations. In this paper, we aim to clarify how primordial information is encoded in maps of galaxies. We use the field-level Cramer-Rao bound to investigate the ultimate limit of what can be extracted from realistic maps of the Universe. For local non-Gaussianity, we show that multi-tracer scale-dependent bias can exceed the sensitivity of conservative higher-point analyses. However, as expected, the multi-tracer analysis falls short of the optimal constraint when all the modes at the scale of the dark matter halos are included. We then forecast the potential reach of future surveys for equilateral and local non-Gaussianity. Equilateral in particular is highly sensitive to priors and modeling assumptions and can benefit dramatically from theoretical input such as the redshift evolution of the bias.