Publications
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The DREAMS project: DaRk mattEr and Astrophysics with Machine learning and Simulations
Rose et al., 2024
The introductory paper for the DREAMS project.
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Can we constrain warm dark matter masses with individual galaxies?
Lin et al., 2024
Taking individual galaxies' properties from the simulations, which have different cosmologies, astrophysics, and assumed warm dark matter masses, we train normalizing flows to infer dark matter properties.
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How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds
Nguyen et al., 2024
In this work, we present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD.
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Inferring warm dark matter masses with deep learning
Rose et al., 2024
The first pre-DREAMS paper. A large suite of dark matter only simulations with Warm Dark Matter were used to test our ability to infer WDM masses using field-level inference.