Quijote-PNG: Quasi-maximum likelihood estimation of Primordial Non-Gaussianity in the non-linear halo density field
Gabriel Jung, Dionysios Karagiannis, Michele Liguori, Marco Baldi,, William R Coulton, Drew Jamieson, Licia Verde, Francisco Villaescusa-Navarro, and Benjamin D. Wandelt

TL;DR
This paper develops and validates a quasi-maximum likelihood estimator for detecting primordial non-Gaussianity in the non-linear halo density field, improving constraints using combined power spectrum and bispectrum analyses.
Contribution
It introduces a new estimator validated on simulations, comparing modal bispectrum and $k$-binning, enhancing the detection of primordial non-Gaussianity signatures.
Findings
Estimator is unbiased and near optimal for local, equilateral, orthogonal PNG.
Modal bispectrum converges faster than $k$-binning, improving constraints.
Forecasted constraints: local $f_{NL}$ around 45, equilateral 570, orthogonal 110.
Abstract
We study primordial non-Gaussian signatures in the redshift-space halo field on non-linear scales, using a quasi-maximum likelihood estimator based on optimally compressed power spectrum and modal bispectrum statistics. We train and validate the estimator on a suite of halo catalogues constructed from the Quijote-PNG N-body simulations, which we release to accompany this paper. We verify its unbiasedness and near optimality, for the three main types of primordial non-Gaussianity (PNG): local, equilateral, and orthogonal. We compare the modal bispectrum expansion with a -binning approach, showing that the former allows for faster convergence of numerical derivatives in the computation of the score-function, thus leading to better final constraints. We find, in agreement with previous studies, that the local PNG signal in the halo-field is dominated by the scale-dependent bias…
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Taxonomy
TopicsComputational Physics and Python Applications · Statistical and numerical algorithms · Geophysics and Gravity Measurements
