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

Autor:	Lovell, Christopher C., Hassan, Sultan, Anglés-Alcázar, Daniel, Bryan, Greg, Fabbian, Giulio, Genel, Shy, Hahn, ChangHoon, Iyer, Kartheik, Kwon, James, de Santi, Natalí, Villaescusa-Navarro, Francisco
Rok vydání:	2023
Předmět:	Astrophysics - Astrophysics of Galaxies
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 2 figures, accepted for ICML 2023 Workshop on Machine Learning for Astrophysics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.06967 Zobrazit plný text záznamu View this record from Arxiv