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Galaxies are biased tracers of the underlying cosmic web, which is dominatedby dark matter components that cannot be directly observed. The relationshipbetween dark matter density fields and galaxy distributions can be sensitive toassumptions in cosmology and astrophysical processes embedded in the galaxyformation models, that remain uncertain in many aspects. Based onstate-of-the-art galaxy formation simulation suites with varied cosmologicalparameters and sub-grid astrophysics, we develop a diffusion generative modelto predict the unbiased posterior distribution of the underlying dark matterfields from the given stellar mass fields, while being able to marginalize overthe uncertainties in cosmology and galaxy formation.

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