Primordial non-Gaussianity inference on
non-linear scales

Simulation based inference

Current and forthcoming cosmological surveys will provide very detailed maps of the large-scale structure of the universe. Vast amounts of data collected from these survey, i.e. all the small scales modes, cannot be analysed using fully analytical models. This constitutes a missed opportunity to shed light on long standing cosmological problems which would benefit from higher-statistical-significance measurements, among which is the determination of primordial non-Gaussianity parameters. Scales that cannot be treated analytically can be modelled with simulations, that we can analyse either with standard statistics or with machine learning techniques. In particular, moment network enables us to perform robust inference without making any assumption about the likelihood, which would also be difficult to treat. We show that the combination of power spectrum and marked power spectrum—and, if available, of the halo mass function—is all we need to successfully constrain the most natural non-Gaussian models. A generalization of our pipeline powered by boosted decision tree quantile regression goes a step further, allowing us to pick up the specific features, or modes, that carry the signal.
References:
G. Jung, AR, M. Liguori, et al., Quijote-PNG: The Information Content of the Halo Mass Function, Astrophys. J. 957 (2023), no. 1 50, [arXiv:2305.10597]
G. Jung, AR, M. Liguori, et al., Quijote-PNG: Optimizing the summary statistics to measure Primordial non-Gaussianity, pre-print, [arXiv:2403.00490]