Quijote-PNG: Quasi-maximum Likelihood Estimation of Primordial Non-Gaussianity in the Nonlinear Dark Matter Density Field

Abstract

Future large-scale structure surveys are expected to improve current bounds on primordial non-Gaussianity (PNG), with a significant impact on our understanding of early universe physics. The level of such improvements will however strongly depend on the extent to which late-time nonlinearities erase the PNG signal on small scales. In this work, we show how much primordial information remains in the bispectrum of the nonlinear dark matter density field by implementing a new, simulation-based methodology for joint estimation of PNG amplitudes (f NL) and standard Lambda cold dark matter parameters. The estimator is based on optimally compressed statistics, which, for a given input density field, combine power spectrum and modal bispectrum measurements, and numerically evaluate their covariance and their response to changes in cosmological parameters. In this first analysis, we focus on the matter density field, and we train and validate the estimator using a large suite of N-body simulations (Quijote-png), including different types of PNG (local, equilateral, orthogonal). We explicitly test the estimator’s unbiasedness, optimality, and stability with respect to changes in the total number of input realizations. While the dark matter power spectrum itself contains negligible PNG information, as expected, including it as an ancillary statistic increases the PNG information content extracted from the bispectrum by a factor of order 2. As a result, we prove the capability of our approach to optimally extract PNG information on nonlinear scales beyond the perturbative regime, up to . At the same time, we discuss the significant information on cosmological parameters contained on these scales.