Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models

TL;DR

This paper introduces a diffusion generative model that reconstructs unbiased dark matter density fields from biased galaxy data, accounting for uncertainties in cosmology and galaxy formation models.

Contribution

It develops a novel probabilistic framework using diffusion models to infer dark matter fields from galaxy data, marginalizing over astrophysical and cosmological uncertainties.

Findings

Successfully reconstructs dark matter fields from galaxy data.

Marginalizes over uncertainties in cosmology and galaxy formation.

Demonstrates robustness across varied simulation parameters.

Abstract

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. The relationship between dark matter density fields and galaxy distributions can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. Based on state-of-the-art galaxy formation simulation suites with varied cosmological parameters and sub-grid astrophysics, we develop a diffusion generative model to predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over the uncertainties in cosmology and galaxy formation.