${\rm S{\scriptsize IM}BIG}$: The First Cosmological Constraints from Non-Gaussian and Non-Linear Galaxy Clustering
ChangHoon Hahn, Pablo Lemos, Liam Parker, Bruno R\'egaldo-Saint, Blancard, Michael Eickenberg, Shirley Ho, Jiamin Hou, Elena Massara, Chirag, Modi, Azadeh Moradinezhad Dizgah, David Spergel

TL;DR
This paper introduces ${\rm S{\scriptsize IM}BIG}$, a novel framework utilizing non-Gaussian galaxy clustering data to obtain tighter cosmological constraints, providing insights into the Hubble constant and matter fluctuations.
Contribution
The paper presents the first cosmological constraints from non-Gaussian and non-linear galaxy clustering using a new framework that combines simulations and deep generative models.
Findings
Constraints on $\Lambda$CDM parameters are significantly tighter than standard power spectrum analyses.
Hubble constant $H_0$ constraints are consistent with early universe measurements.
$S_8$ constraints align with weak lensing results and are below CMB constraints.
Abstract
The 3D distribution of galaxies encodes detailed cosmological information on the expansion and growth history of the Universe. We present the first cosmological constraints that exploit non-Gaussian cosmological information on non-linear scales from galaxy clustering, inaccessible with current standard analyses. We analyze a subset of the BOSS galaxy survey using , a new framework for cosmological inference that leverages high-fidelity simulations and deep generative models. We use two clustering statistics beyond the standard power spectrum: the bispectrum and a convolutional neural network based summary of the galaxy field. We infer constraints on CDM parameters, , , , , and , that are 1.6, 1.5, 1.7, 1.2, and 2.3 tighter than power spectrum analyses. With this increased precision, we derive constraints…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Computational Physics and Python Applications
