arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.04565astro-ph.HE
收藏

利用机器学习以类毫秒脉冲星源解决银河系中心GeV超出的问题

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

Dmitry V. Malyshev

AI总结:

本文利用机器学习对Fermi-LAT源进行多类分类,评估银河系中心附近未关联的类毫秒脉冲星源对GeV伽马射线超出的贡献,发现其能谱、形态和源计数分布与MSP群体解释一致。

摘要:

费米大面积望远镜(Fermi-LAT)数据中观测到的银河系中心(GC)周围伽马射线超出是伽马射线天空中最引人注目的特征之一。其球形形态和峰值在几GeV附近的能谱分布与暗物质粒子湮灭产生的辐射一致。其他可能的解释包括毫秒脉冲星(MSPs)的分布。MSP假说的一个不足之处是GC附近已确认关联的MSP数量相对较少。本文利用机器学习对Fermi-LAT源进行多类分类,并确定GC附近未关联的类MSP源的贡献。其能谱分布、空间形态和源计数分布与能够解释伽马射线超出的MSP群体的预期一致。文中还讨论了未关联类MSP源贡献可能存在的局限性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑