Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations. Rose, J. C., Torrey, P., Farahi, A., Kallivayalil, N., Muñoz, J. B., Garcia, A. M., Villaescusa-Navarro, F., Lisanti, M., Nguyen, T., Roy, S., Kollmann, K. E., Vogelsberger, M., Cyr-Racine, F., Medvedev, M. V., Genel, S., Anglés-Alcázar, D., Wang, B. Y., Costanza, B., O’Neil, S., Roche, C., Karmakar, S., Low, R., Lin, S., Mostow, O., Cruz, A., Caputo, A., Necib, L., Teyssier, R., Dalcanton, J. J., & Spergel, D. The Astrophysical Journal, 982(2):68, April, 2025. ![Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine Learning and Simulations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ( 25 h − 1 Mpc ) 3 volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales.
@article{rose_introducing_2025,
title = {Introducing the {DREAMS} {Project}: {DaRk} {mattEr} and {Astrophysics} with {Machine} {Learning} and {Simulations}},
volume = {982},
issn = {0004-637X, 1538-4357},
shorttitle = {Introducing the {DREAMS} {Project}},
url = {https://iopscience.iop.org/article/10.3847/1538-4357/adb8e5},
doi = {10.3847/1538-4357/adb8e5},
abstract = {Abstract
We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the
arepo
code. One suite consists of uniform-box simulations covering a
(
25
h
−
1
Mpc
)
3
volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales.},
language = {en},
number = {2},
urldate = {2025-05-28},
journal = {The Astrophysical Journal},
author = {Rose, Jonah C. and Torrey, Paul and Farahi, Arya and Kallivayalil, Nitya and Muñoz, Julian B. and Garcia, Alex M. and Villaescusa-Navarro, Francisco and Lisanti, Mariangela and Nguyen, Tri and Roy, Sandip and Kollmann, Kassidy E. and Vogelsberger, Mark and Cyr-Racine, Francis-Yan and Medvedev, Mikhail V. and Genel, Shy and Anglés-Alcázar, Daniel and Wang, Bonny Y. and Costanza, Belén and O’Neil, Stephanie and Roche, Cian and Karmakar, Soumyodipta and Low, Ryan and Lin, Shurui and Mostow, Olivia and Cruz, Akaxia and Caputo, Andrea and Necib, Lina and Teyssier, Romain and Dalcanton, Julianne J. and Spergel, David},
month = apr,
year = {2025},
pages = {68},
}
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Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ( 25 h − 1 Mpc ) 3 volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. 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