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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Probabilistic Reconstruction of Dark Matter fields from galaxies using diffusion models

Carolina Cuesta · Yueying Ni · Core Francisco Park · Nayantara Mudur · Victoria Ono


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 to be 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 marginalizing over the uncertainties lying in cosmology and galaxy formation.

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