Publications
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The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties
The first presentation paper for the CDM MW DREAMS simulation suite, exploring the variation in MW properties and the influence of modeling choices versus formation history.
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The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Satellite Galaxies
As a key demonstration of the DREAMS CDM suite, this paper explores the impact of adopted astrophysical model parameters on the MW satellite galaxy population.
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The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles
An exploration of the variation in Dark Matter density profiles and their dependence on astrophysics and cosmology parameters.
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The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Speed Distributions
Lilie et al., 2025
A characterization of the dark matter speed distributions and their dependence on Halo Mass, as well as the astrophysics parameters varied within DREAMS. -
Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks
A study of the use of Graph Neural Networks to infer the cosmology and astrophysical parameters that used to create the merger trees.
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On the sensitivity of different galaxy properties to warm dark matter
A study of the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 cosmological hydrodynamical simulations from the DREAMS project
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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.