Francisco Villaescusa-Navarro

Inductive Biases in Field-level Cosmological Inference from Galaxy Catalogs

Abstract We perform field-level likelihood-free inference of the matter density parameter Ω m from simulated galaxy catalogs using machine learning models with differing inductive biases. Using hydrodynamic simulations from CAMELS, we examine how observable choice and architectur...

Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters

Abstract We present a new method for emulating the halo mass function (HMF) and other distribution functions in large effective volumes, down to low halo masses, whilst simultaneously modifying large ranges of parameters, for a fraction of the cost of traditional periodic cosmolo...