A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

Christopher C. Lovell; Sultan Hassan; Daniel Anglés-Alcázar; Greg Bryan; Giulio Fabbian; Shy Genel; ChangHoon Hahn; Kartheik Iyer; James Kwon; Natalí de Santi; Francisco Villaescusa-Navarro

arXiv · 2307.06967

A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

Christopher C. Lovell·Sultan Hassan·Daniel Anglés-Alcázar·Greg Bryan·Giulio Fabbian·Shy Genel·ChangHoon Hahn·Kartheik Iyer·James Kwon·Natalí de Santi·Francisco Villaescusa-Navarro

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Abstract

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship.

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Christopher C. Lovell, Sultan Hassan, Daniel Anglés-Alcázar, Greg Bryan, Giulio Fabbian, Shy Genel, ChangHoon Hahn, Kartheik Iyer, James Kwon, Natalí de Santi, Francisco Villaescusa-Navarro. 2023-07-13. A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship. https://arxiv.org/abs/2307.06967

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