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

Fuente: arXiv
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Main Author: Malyshev, Dmitry V.
Format: Preprint
Published: 2024
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author Malyshev, Dmitry V.
author_facet Malyshev, Dmitry V.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_04565
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Towards resolving the Galactic center GeV excess with millisecond-pulsar-like sources using machine learning
Malyshev, Dmitry V.
High Energy Astrophysical Phenomena
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.
title Towards resolving the Galactic center GeV excess with millisecond-pulsar-like sources using machine learning
topic High Energy Astrophysical Phenomena
url https://arxiv.org/abs/2401.04565