Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS

TL;DR

This paper introduces a diffusion generative model trained on diverse galaxy formation simulations to accurately reconstruct dark matter fields from galaxy data, effectively marginalizing over model uncertainties and generalizing to larger volumes and different models.

Contribution

The authors develop a diffusion model that reconstructs dark matter fields from galaxies, capable of handling uncertainties and generalizing beyond training data.

Findings

Accurately predicts unbiased dark matter fields from galaxy data.

Generalizes to larger simulation volumes and different galaxy formation models.

Marginalizes over cosmological and astrophysical uncertainties.

Abstract

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between dark matter density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct dark matter fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and sub-grid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes approximately 500 times larger than those it was trained on, and across different galaxy formation models. Code for reproducing these results can be found at https://github.com/victoriaono/variational-diffusion-cdm