36 citations · 46 across the 3 of their papers we have counts for
3 papers
Learning the Universe: physically-motivated priors for dust attenuation curves
Laura Sommovigo, Rachel K. Cochrane, Rachel S. Somerville +9
Understanding the impact of dust on the spectral energy distributions (SEDs) of galaxies is crucial for inferring their physical properties and for studying the nature of interstel…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo +11
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood…