Associating γ -ray sources to their low-energy counterparts is one of the major challenges of modern γ -ray astronomy. In the context of the Fourth Fermi Large Area Telescope Source Catalog (4FGL), the associations rely mainly on parameters such as apparent magnitude, integrated flux, and angular separation between the γ -ray source and its low-energy candidate counterpart. In this work, we propose a new use of the likelihood ratio (LR) and a complementary supervised learning technique to associate γ -ray blazars in 4FGL, based only on spectral parameters such as the γ -ray photon index, mid-infrared colors, and radio-loudness. In the LR approach, we crossmatch the Wide-field Infrared Survey Explorer Blazar-Like Radio-Loud Sources catalog with 4FGL and compare the resulting candidate counterparts with the sources listed in the γ -ray blazar locus to compute an association probability (AP) for 1138 counterparts. In the supervised learning appro...

中文翻译：

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费米-拉特轻质低能生物的物理缔合

将γ射线源与其低能对应物联系在一起是现代γ射线天文学的主要挑战之一。在第四部费米大面积望远镜震源目录（4FGL）的背景下，这些关联主要依赖于诸如视在震级，积分通量以及γ射线源与其低能候选者之间的角距等参数。在这项工作中，我们仅基于诸如γ射线光子指数，中红外色等光谱参数，提出了似然比（LR）和辅助监督学习技术的新用途，以关联4FGL中的γ射线危险。和无线电响度。在LR方法中，我们将宽视野红外测量浏览器Blazar-Like无线电源目录与4FGL进行交叉匹配，并将得到的候选对应物与γ射线blazar位点中列出的源进行比较，以计算1138个对应物的关联概率（AP）。在监督学习中...