Machine-learned dark matter subhalo candidates in the 4FGL-DR2: search for the perturber of the GD-1 stream

@article{Mirabal2021MachinelearnedDM,
  title={Machine-learned dark matter subhalo candidates in the 4FGL-DR2: search for the perturber of the GD-1 stream},
  author={N. Mirabal and Ana Bonaca},
  journal={Journal of Cosmology and Astroparticle Physics},
  year={2021},
  volume={2021}
}
  • N. Mirabal, A. Bonaca
  • Published 25 May 2021
  • Physics
  • Journal of Cosmology and Astroparticle Physics
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… Expand

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