Aggressively Dissipative Dark Dwarfs: The Effects of Atomic Dark Matter on the Inner Densities of Isolated Dwarf Galaxies

Creators: Roy, Sandip¹; Shen, Xuejian²; Barron, Jared³; Lisanti, Mariangela^{1, 4}; Curtin, David⁵; Murray, Norman⁶; Hopkins, Philip F.⁷

1. Princeton University
2. Massachusetts Institute of Technology
3. Stony Brook University
4. Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA
5. University of Toronto
6. Canadian Institute for Theoretical Astrophysics
7. California Institute of Technology

Abstract

We present the first suite of cosmological hydrodynamical zoom-in simulations of isolated dwarf galaxies for a dark sector that consists of cold dark matter and a strongly dissipative subcomponent. The simulations are implemented in GIZMO and include standard baryons following the FIRE-2 galaxy formation physics model. The dissipative dark matter is modeled as atomic dark matter (aDM), which forms a dark hydrogen gas that cools in direct analogy to the Standard Model. Our suite includes seven different simulations of ∼10¹⁰ M_⊙ systems that vary over the aDM microphysics and the dwarf's evolutionary history. We identify a region of aDM parameter space where the cooling rate is aggressive and the resulting halo density profile is universal. In this regime, the aDM gas cools rapidly at high redshifts, and only a small fraction survives in the form of a central dark gas disk; the majority collapses centrally into collisionless dark "clumps," which are clusters of subresolution dark compact objects. These dark clumps rapidly equilibrate in the inner galaxy, resulting in an approximately isothermal distribution that can be modeled with a simple fitting function. Even when only a small fraction (∼5%) of the total dark matter is strongly dissipative, the central densities of classical dwarf galaxies can be enhanced by over an order of magnitude, providing a sharp prediction for observations.

Copyright and License

© 2025. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Acknowledgement

The authors would like to acknowledge helpful conversations and feedback from Arpit Arora, Dylan Folsom, Zachary Gelles, Caleb Gemmell, Akshay Ghalsasi, James Gurian, Abdelaziz Hussein, Mariia Khelashvili, Lina Necib, Michael Ryan, Robyn Sanderson, Sarah Schon, Sarah Shandera, Maya Silverman, Oren Slone, and Linda Yuan.

S.R. and M.L. are supported by the National Science Foundation (NSF), under award No. AST 2307789, and by the Department of Energy (DOE), under award No. DE-SC0007968. M.L. is also supported by the Simons Investigator in Physics Award. This research was supported in part by grant NSF PHY-2309135 to the Kavli Institute for Theoretical Physics (KITP). N.W.M. acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2023-04901). Support for PFH was provided by NSF Research Grants 20009234, 2108318, NASA grant 80NSSC18K0562, and a Simons Investigator Award. D.C. was supported in part by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chair program, as well as the Alfred P. Sloan Foundation, the Ontario Early Researcher Award, and the University of Toronto McLean Award. This work was performed in part at the Aspen Center for Physics, which is supported by National Science Foundation grant PHY-2210452. Numerical simulations were run on the supercomputer Frontera at the Texas Advanced Computing Center (TACC) under the allocations AST21010 and AST23013 supported by the NSF and TACC, and NASA HEC SMD-16-7592. This research is part of the Frontera computing project at the Texas Advanced Computing Center. Frontera is made possible by National Science Foundation award OAC-1818253. Analysis of the simulations was performed on the Niagara supercomputer at the SciNet HPC Consortium. SciNet is funded by Innovation, Science and Economic Development Canada; the Digital Research Alliance of Canada; the Ontario Research Fund: Research Excellence; and the University of Toronto. The work reported on in this paper was also partly performed using the Princeton Research Computing resources at Princeton University, which is consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing.

Code Availability

This research made extensive use of the publicly available codes IPython (F. Pérez & B. E. Granger 2007), Jupyter (T. Kluyver et al. 2016), matplotlib (J. D. Hunter 2007), NumPy (C. R. Harris et al. 2020), scikit-learn (F. Pedregosa et al. 2011), SciPy (P. Virtanen et al. 2020), SWIFTsimIO (J. Borrow & A. Borrisov 2020), unyt (N. J. Goldbaum et al. 2018), gizmo-analysis (A. Wetzel & S. Garrison-Kimmel 2020), and Python Imaging Library (A. Clark 2015).

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