论文标题
识别3FHL目录。 vi。通过机器学习对3FHL无关的对象进行迅速观察
Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning
论文作者
论文摘要
硬费米大面积望远镜来源（3FHL）的第三目报告在E> 10 GEV时检测到1556个物体。然而，177个来源保持无关，23个与Rosat X射线检测未知来源有关。对30个与Swift-XRT的无关和未知来源进行了尖X射线观测。在30个场中的21个场中检测到了一个明亮的X射线源对应物。在这21个字段中的五个中，我们检测到了一个以上的X射线对应物，总共分析了26个X射线源。针对检测到的每个X射线源编译了多波长度数据。我们发现，检测到的26个X射线源中有21个显示了Blazars的多波长特性，而一个X射线源显示了Galactic源的特性。使用训练有素的决策树，随机森林和支持向量机模型，我们预测所有21种Blazar对应物候选者都是Bl lacertae对象（BL LACS）。这与BL LACS是3FHL人口最多的来源类一致。
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.
