Quijote-png: Quasi-maximum likelihood estimation of primordial non-gaussianity in the nonlinear dark matter density field
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
American Astronomical Society
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 ( fNL)
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).
Description
Keywords
Non-gaussianity, Astronomy, Physics, Cosmology, Astrophysics
Citation
Jung, G. et al. (2022). Quijote-png: Quasi-maximum likelihood estimation of primordial non-gaussianity in the nonlinear dark matter density field. Astrophysical Journal, 940 71. 10.3847/1538-4357/ac9837