@article{3ed6922f289c47a2983a273ecb67c92f,
title = "A preference for cold dark matter over Superfluid Dark Matter in local Milky Way data",
abstract = "There are many well-known correlations between dark matter and baryons that exist on galactic scales. These correlations can essentially be encompassed by a simple scaling relation between observed and baryonic accelerations, historically known as the Mass Discrepancy Acceleration Relation (MDAR). The existence of such a relation has prompted many theories that attempt to explain the correlations by invoking additional fundamental forces on baryons. The standard lore has been that a theory that reduces to the MDAR on galaxy scales but behaves like cold dark matter (CDM) on larger scales provides an excellent fit to data, since CDM is desirable on scales of clusters and above. However, this statement should be revised in light of recent results showing that a fundamental force that reproduces the MDAR is challenged by local Milky Way dynamics and rotation curve data between 5–18 kpc. In this study, we test this claim on the example of Superfluid Dark Matter. We find that a standard CDM model is preferred over a static superfluid profile assuming a steady-state Galactic disk and discuss the robustness of this conclusion to disequilibrium effects. This preference is due to the fact that the superfluid model over-predicts vertical accelerations, even while reproducing galactic rotation curves. Our results establish an important criterion that any dark matter model must satisfy within the Milky Way.",
keywords = "Dark matter, Gaia, MDAR, MOND, Superfluid Dark Matter",
author = "Mariangela Lisanti and Matthew Moschella and Outmezguine, {Nadav Joseph} and Oren Slone",
note = "Funding Information: We thank J. Khoury for integral feedback over the course of this work. We also thank M. Geller, M. Milgrom, S. McGaugh, H. Verlinde, and T. Volansky for useful conversations. ML is supported by the DOE under contract DESC0007968 and the Cottrell Scholar Program through the Research Corporation for Science Advancement. MM is supported by the DOE under contract DESC0007968. NJO is supported by the Azrieli Foundation Fellows program . The work presented in this paper was performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology{\textquoteright}s High Performance Computing Center and Visualization Laboratory at Princeton University. This research made use of the Astropy [60] , IPython [61] , matplotlib [62] , numpy [63] , galpy [64] , corner [65] , and MultiNest [57,58] software packages. Funding Information: We thank J. Khoury for integral feedback over the course of this work. We also thank M. Geller, M. Milgrom, S. McGaugh, H. Verlinde, and T. Volansky for useful conversations. ML is supported by the DOE under contract DESC0007968 and the Cottrell Scholar Program through the Research Corporation for Science Advancement. MM is supported by the DOE under contract DESC0007968. NJO is supported by the Azrieli Foundation Fellows program. The work presented in this paper was performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology's High Performance Computing Center and Visualization Laboratory at Princeton University. This research made use of the Astropy [60], IPython [61], matplotlib [62], numpy [63], galpy [64], corner [65], and MultiNest [57,58] software packages. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2023",
month = feb,
doi = "10.1016/j.dark.2022.101140",
language = "English (US)",
volume = "39",
journal = "Physics of the Dark Universe",
issn = "2212-6864",
publisher = "Elsevier BV",
}