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

TL;DR

This paper uses machine learning to classify Fermi-LAT gamma-ray sources and assess whether unassociated millisecond pulsar-like sources can explain the Galactic center GeV excess, supporting the MSP hypothesis.

Contribution

It introduces a multiclass machine learning classification of gamma-ray sources to evaluate the MSP contribution to the Galactic center excess.

Findings

Spectral and spatial properties match MSP expectations.

Unassociated MSP-like sources could account for the gamma-ray excess.

Discussion of potential limitations of the MSP explanation.

Abstract

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.