Galactic center GeV excess and classification of Fermi-LAT sources with machine learning
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909218406137856 |
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| author | Malyshev, Dmitry V. |
| author_facet | Malyshev, Dmitry V. |
| contents | Excess of gamma rays with a spherical morphology 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 excess has been interpreted by annihilating dark matter as well as emission from a population of unresolved millisecond pulsars (MSPs). We use a multi-class classification of Fermi-LAT sources with machine learning to study the distribution of MSP-like sources among unassociated Fermi-LAT sources near the GC. We find that the source count distribution of MSP-like sources is comparable with the MSP explanation of the GC excess. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Galactic center GeV excess and classification of Fermi-LAT sources with machine learning Malyshev, Dmitry V. High Energy Astrophysical Phenomena Excess of gamma rays with a spherical morphology 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 excess has been interpreted by annihilating dark matter as well as emission from a population of unresolved millisecond pulsars (MSPs). We use a multi-class classification of Fermi-LAT sources with machine learning to study the distribution of MSP-like sources among unassociated Fermi-LAT sources near the GC. We find that the source count distribution of MSP-like sources is comparable with the MSP explanation of the GC excess. |
| title | Galactic center GeV excess and classification of Fermi-LAT sources with machine learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2406.03990 |