Contamination of stellar-kinematic samples and uncertainty about dark matter annihilation profiles in ultrafaint dwarf galaxies: the example of Segue I
V. Bonnivard, D. Maurin, M. G. Walker

TL;DR
This paper investigates how contamination in stellar-kinematic samples affects dark matter density estimates in ultrafaint dwarf galaxies, highlighting the potential for significant overestimation of gamma-ray signals from dark matter annihilation.
Contribution
It demonstrates that contamination can cause large overestimations of J-factors and emphasizes the need for careful analysis of stellar membership to accurately interpret dark matter signals.
Findings
Contamination can overestimate J-factors by orders of magnitude.
Segue I's J-factor estimates are particularly sensitive to stellar membership uncertainties.
Robust dark matter inferences require explicit contamination analysis.
Abstract
The expected gamma-ray flux coming from dark matter annihilation in dwarf spheroidal (dSph) galaxies depends on the so-called `J-factor', the integral of the squared dark matter density along the line-of-sight. We examine the degree to which estimates of J are sensitive to contamination (by foreground Milky Way stars and stellar streams) of the stellar-kinematic samples that are used to infer dark matter densities in `ultrafaint' dSphs. Applying standard kinematic analyses to hundreds of mock data sets that include varying levels of contamination, we find that mis-classified contaminants can cause J-factors to be overestimated by orders of magnitude. Stellar-kinematic data sets for which we obtain such biased estimates tend 1) to include relatively large fractions of stars with ambiguous membership status, and 2) to give estimates for J that are sensitive to specific choices about how…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
