Machine-Learned Dark Matter Subhalo Candidates in the 4FGL-DR2: Search for the Perturber of the GD-1 Stream
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2021-11-15
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Citation of Original Publication
Mirabal, Nestor and Bonaca, Ana." Machine-Learned Dark Matter Subhalo Candidates in the 4FGL-DR2: Search for the Perturber of the GD-1 Stream." Journal of Cosmology and Astroparticle Physics, vol 2021 (15 November 2021). https://doi.org/10.1088/1475-7516/2021/11/033
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Abstract
The detection of dark matter subhalos without a stellar component in the Galactic halo remains a challenge. We use supervised machine learning to identify high-latitude gamma-ray sources with dark matter-like spectra among unassociated gamma-ray sources in the 4FGL-DR2. Out of 843 4FGL-DR2 unassociated sources at |b|≥10∘, we select 73 dark matter subhalo candidates. Of the 69 covered by the Neil Gehrels Swift Observatory (Swift), 17 show at least one X-ray source within the 95% LAT error ellipse and 52 where we identify no new sources. This latest inventory of dark subhalos candidates allows us to investigate the possible dark matter substructure responsible for the perturbation in the GD-1 stellar stream. In particular, we examine the possibility that the alleged GD-1 dark subhalo may appear as a 4FGL-DR2 gamma-ray source from dark matter annihilation into Standard Model particles.