A hierarchy of normalizing flows for modelling the galaxy-halo relationship

Christopher C. Lovell, Sultan Hassan, Daniel Anglés-Alcázar, Greg Bryan, G. 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.
Original languageEnglish
Number of pages8
DOIs
Publication statusPublished - 13 Jun 2023
EventMachine Learning for Astrophysics Workshop at the Fortieth International Conference on Machine Learning (ICML 2023) - Hawaii
Duration: 29 Jul 202329 Jul 2023
https://ml4astro.github.io/icml2023/#rationale

Conference

ConferenceMachine Learning for Astrophysics Workshop at the Fortieth International Conference on Machine Learning (ICML 2023)
Abbreviated titleICML 2023
Period29/07/2329/07/23
Internet address

Keywords

  • astro-ph.GA

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