Dark Matter Subhalo interpretations using machine learning: The fourth Fermi-LAT catalog

The quest for detecting dark-matter subhalos within the Galactic halo has taken many forms. Particularly interesting and promising is the use of spectral degeneracies to distinguish otherwise indistinguishable gamma-ray sources with near-null star formation. In further exploration of this realm, we attempt to classify high-latitude, non-variable, unassociated gamma-ray sources with Pulsar-like spectra in the 20-70 GeV Dark Matter annihilation range. Implementing supervised machine learning models on the 5788 gamma-ray sources recorded in the ten-year Fermi -LAT catalog (4FGL-DR2), where 1667 were formerly unassociated, we classify a total of 30 recorded gamma-ray events over a galactic latitude of 10 degrees, |b| >= 10 with a mean accuracy over 98%. This classification allows us to present a subset of potentially unanticipated gamma-ray sources as high-confidence Dark Matter Subhalo candidates. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

By using spectral degeneracies to distinguish indistinguishable gamma-ray sources, we classified high-latitude, non-variable, unassociated gamma-ray sources and identified a subset of potential dark matter subhalo candidates.