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Constraints on Dark Matter Microphysics from the Milky Way Satellite Population

2019/04/30 by Ethan O. Nadler, Vera Gluscevic, Kimberly K. Boddy +1 · 1 citation
Physics and Astronomy · #astro-ph.CO #hep-ph

paper · pdf · doi:10.3847/2041-8213/ab1eb2

published as ApJL 878, 32 (2019) · 6 pages, 2 figures. Updated to reflect erratum due to an erroneous prefactor in our modified CLASS code that enhanced our cross section constraints by a factor of ~4 at all dark matter masses. Code available at http://github.com/eonadler/DMBaryonScattering

arxiv created 2020/06/16 · arxiv updated 2020/06/18

Abstract

Alternatives to the cold, collisionless dark matter (DM) paradigm in which DM behaves as a collisional fluid generically suppress small-scale structure. Herein we use the observed population of Milky Way (MW) satellite galaxies to constrain the collisional nature of DM, focusing on DM-baryon scattering. We first derive analytic upper limits on the velocity-independent DM-baryon scattering cross section by translating the upper bound on the lowest mass of halos inferred to host satellites into a characteristic cutoff scale in the linear matter power spectrum. We then confirm and improve these results through a detailed probabilistic inference of the MW satellite population that marginalizes over relevant astrophysical uncertainties. This yields 95% confidence upper limits on the DM-baryon scattering cross section of 2×10-29 \rmcm2 (6× 10-27 \rmcm2) for DM particle masses mχ of~10 \rmkeV (10 \rmGeV); these limits scale as mχ1/4 for mχ≪ 1 \rmGeV and mχ for~mχ≫ 1 \rmGeV. This analysis improves upon cosmological bounds derived from cosmic-microwave-background anisotropy measurements by multiple orders of magnitude over a wide range of DM masses, excluding regions of parameter space previously unexplored by other methods, including direct-detection experiments. Our work reveals a mapping between DM-baryon scattering and other alternative DM models, and we discuss the implications of our results for warm and fuzzy DM scenarios.

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