Towards resolving the Galactic center GeV excess with millisecond-pulsar-like sources using machine learning

Excess of gamma rays around the Galactic center (GC) observed in the Fermi Large Area Telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The spherical morphology and the spectral energy distribution with a peak around a few GeV are consistent with emission from annihilation of dark matter particles. Other possible explanations include a distribution of millisecond pulsars (MSPs). One of the caveats of the MSP hypothesis is the relatively small number of associated MSPs near the GC. In this paper, we perform a multiclass classification of Fermi-LAT sources using machine learning and determine the contribution from unassociated MSP-like sources near the GC. The spectral energy distribution, spatial morphology, and the source count distribution are consistent with expectations for a population of MSPs that can explain the gamma-ray excess. Possible caveats of the contribution from the unassociated MSP-like sources are discussed.