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Semi-analytic Inference of Dwarf Galaxy Densities and Implications for Dark Matter Searches

Kailash Raman · CERN

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About

Ultra-faint dwarf galaxies are critical testing grounds for the particle nature of dark matter. In this talk, I will present Milky Way dwarf density profiles inferred using a semi-analytic satellite generator that captures the Cold Dark Matter (CDM) halo population. We match halos to dwarfs via two criteria: a kinematic selection (using enclosed mass at the half-light radius) and a stellar-mass selection (via a galaxy formation model). The kinematic selection yields larger diversity in central densities than CDM predicts—the most compact ultra-faints are denser, the most diffuse less dense—a discrepancy robust to all systematic variations explored. I will also present updated astrophysical $J$-factors using the latest kinematic data. We find that observed Milky Way satellites set stronger annihilation constraints than a typical Milky Way–mass system, excluding a thermal relic cross section for annihilation to $b\bar{b}$ below 173 GeV at 95% confidence. A completeness analysis suggests future ultra-faint discoveries are unlikely to substantially strengthen these limits.

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The talk

  1. Semi-analytic Inference of Dwarf Galaxy Densities and Implications for Dark Matter Searches 2026-07-20 · 10 min · CERN's player

Credits and licence

Institution
CERN
Speaker
Kailash Raman
Year
2026
Language
English
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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