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arXiv 2212.02579astro-ph.HEastro-ph.GA
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识别3FHL目录。VI。基于机器学习的3FHL无关联目标快速观测与源分类

Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning

S. Joffre, R. Silver, M. Rajagopal, M. Ajello, N. Torres-Albà, A. Pizzetti, S. Marchesi, A. Kaur

摘要:

第三个硬费米大面积望远镜源目录(3FHL)报告了1556个E > 10 GeV的天体。然而,仍有177个放射源没有关联,23个放射源与来源不明的ROSAT X射线探测有关。用Swift-XRT对其中30个无关和未知的源进行了定点X射线观察。在30个视场中的21个视场中检测到明亮的X射线源对应物。在这21个领域中的5个领域,我们检测到一个以上的X射线对应物,共分析了26个X射线源。多波长数据被编译为每个X射线源检测。我们发现,26个X射线源中的21个显示耀变体的多波长特性,而一个X射线源显示银河系源的特性。使用训练的决策树,随机森林和支持向量机模型,我们预测所有21 blazar对应候选人BL Lacertae对象(BL Lacs)。这与BL Lac是3FHL中人口最多的源类是一致的。

英文摘要:

The Third Catalog of Hard Fermi Large Area Telescope Sources (3FHL) reports the detection of 1556 objects at E > 10 GeV. However, 177 sources remain unassociated and 23 are associated with a ROSAT X-ray detection of unknown origin. Pointed X-ray observations were conducted on 30 of these unassociated and unknown sources with Swift-XRT. A bright X-ray source counterpart was detected in 21 out of 30 fields. In five of these 21 fields, we detected more than one X-ray counterpart, totaling 26 X-ray sources analyzed. Multiwavelength data was compiled for each X-ray source detected. We find that 21 out of the 26 X-ray sources detected display the multiwavelength properties of blazars, while one X-ray source displays the characteristics of a Galactic source. Using trained decision tree, random forest, and support vector machine models, we predict all 21 blazar counterpart candidates to be BL Lacertae objects (BL Lacs). This is in agreement with BL Lacs being the most populous source class in the 3FHL.

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。

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