Abstract: The parameter fNL measures the local non-Gaussianity in the primordial energy fluctuations of the Universe, with any deviation from fNL = 0 providing key constraints on inflationary models. Galaxy clustering is sensitive to fNL at large scale modes and the next generation of galaxy surveys will approach a statistical error of σfNL ∼ 1. However, the systematic errors on these constraints are dominated by the degeneracy of fNL with the galaxy bias parameters b1 (galaxy overdensities caused by mass perturbations) and bϕ (galaxy overdensities caused by primordial potential perturbations). It has been shown that the assumed "universality" scaling of bϕ(z) = 2δc(b1(z) − 1) is not accurate for realistically simulated galaxies, and depends both on the galaxy selection and the way that galaxies are modeled. To address this, we leverage the CAMELS-SAM pipeline to explore how varying parameters of galaxy formation affects bϕ and b1 for various galaxy selections. We run separate-universe N-body simulations of L = 205h−1 cMpc and N = 1280^3 to measure bϕ, and run 55 unique instances of the Santa Cruz semi-analytic model with varying parameters of stellar and AGN feedback. We find the behavior and evolution of a SC-SAM model’s stellar-, SFR- and sSFR- to halo mass relationships track well with how b1 and bϕ(b1) change across redshift and selection for the SC-SAM. We find our variations of the SC-SAM encapsulate the bϕ behavior previously measured in IllustrisTNG, the Munich SAM, and Galacticus. Finally, we apply these results to yield informed priors on bϕ conditioned on galaxy observations for fNL measurements.

Speaker Bio: Lucia Perez is a Flatiron Research Fellow at the Center for Computational Astrophysics (CCA) at the Flatiron Institute, and also a Future Faculty in the Physical Sciences postdoctoral Fellow at Princeton University. Perez completed her Ph.D. in astrophysics from Arizona State University in 2022, focusing on the clustering of Lyman Alpha Emitters and reionization. During this time she mostly worked out of NASA Goddard Space Flight Center thanks to the development of the Roman Space Telescope. Perez was a part of the CCA pre-doctoral program in 2020, where she created the CAMELS-SAM galaxy simulation suite for machine learning. This line of research led to her current focus on "building weird galaxies in weird universes", in which she creates galaxy simulations and mock observational catalogs at scale in order to constrain cosmology and astrophysics with simulation-based inference.