Machine-Learned Dark Matter Subhalo Candidates in the 4FGL-DR2: Search for the Perturber of the GD-1 Stream

Understand

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 $|b| \geq 10\mathrm{^\circ}$, 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.