当前位置: X-MOL 学术Res. Astron. Astrophys. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning
Research in Astronomy and Astrophysics ( IF 1.8 ) Pub Date : 2021-02-02 , DOI: 10.1088/1674-4527/21/1/15
Ke-Rui Zhu 1 , Shi-Ju Kang 2 , Yong-Gang Zheng 1
Affiliation  

In the fourth Fermi Large Area Telescope source catalog (4FGL), 5064 γ-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazars at low Galactic latitudes and 190 other sources. We employ two different supervised machine learning classifiers, combined with the direct observation parameters given by the 4FGL fits table, to search for sources potentially classified as AGNs and pulsars in the 1336 unassociated sources. In order to reduce the error caused by the large difference in the sizes of samples, we divide the classification process into two separate steps in order to identify the AGNs and the pulsars. First, we select the identified AGNs from all of the samples, and then select the identified pulsars from the remaining cases. Using the 4FGL sources associated or identified as AGNs, pulsars and other sources with the features selected through the K-S test and the random forest (RF) feature importance measurement, we trained, optimized and tested our classifier models. Then, the models are applied to classify the 1336 unassociated sources. According to the calculation results of the two classifiers, we report the sensitivity, specificity, accuracy in each step and the class of unassociated sources given by each classifier. The accuracy obtained in the first step is approximately 95%; in the second step, the obtained overall accuracy is approximately 80%. Combining the results of the two classifiers, we predict that there are 583 AGN-type candidates, 115 pulsar-type candidates, 154 other types of γ-ray candidates and 484 of uncertain types.



中文翻译:

使用机器学习在 4FGL 非关联源中搜索 AGN 和脉冲星候选者

在第四个费米大面积望远镜源目录(4FGL)中,5064 γ报告的射线源包括 3207 个活动星系核 (AGN)、239 个脉冲星、1336 个非关联源、92 个与低银河系耀变体弱关联的源和 190 个其他源。我们采用两种不同的监督机器学习分类器,结合 4FGL 拟合表给出的直接观测参数,在 1336 个非关联源中搜索可能分类为 AGN 和脉冲星的源。为了减少样本大小差异过大造成的误差,我们将分类过程分为两个独立的步骤,以识别活动星系核和脉冲星。首先,我们从所有样本中选择已识别的活动星系核,然后从剩余案例中选择已识别的脉冲星。使用关联或识别为 AGN 的 4FGL 源,脉冲星和其他具有通过 KS 测试和随机森林 (RF) 特征重要性测量选择的特征的源,我们训练、优化和测试了我们的分类器模型。然后,将模型应用于对 1336 个不相关的源进行分类。根据两个分类器的计算结果,我们报告了每个步骤中的敏感性、特异性、准确性以及每个分类器给出的非关联源的类别。第一步得到的准确率约为95%;在第二步中,获得的整体准确率约为 80%。结合两个分类器的结果,我们预测有 583 个 AGN 型候选,115 个脉冲星型候选,154 个其他类型 优化和测试了我们的分类器模型。然后,将模型应用于对 1336 个不相关的源进行分类。根据两个分类器的计算结果,我们报告了每个步骤中的敏感性、特异性、准确性以及每个分类器给出的非关联源的类别。第一步得到的准确率约为95%;在第二步中,获得的整体准确率约为 80%。结合两个分类器的结果,我们预测有 583 个 AGN 型候选,115 个脉冲星型候选,154 个其他类型 优化和测试了我们的分类器模型。然后,将模型应用于对 1336 个不相关的源进行分类。根据两个分类器的计算结果,我们报告了每个步骤中的敏感性、特异性、准确性以及每个分类器给出的非关联源的类别。第一步得到的准确率约为95%;在第二步中,获得的整体准确率约为 80%。结合两个分类器的结果,我们预测有 583 个 AGN 型候选,115 个脉冲星型候选,154 个其他类型 每个步骤的准确性和每个分类器给出的非关联源的类别。第一步得到的准确率约为95%;在第二步中,获得的整体准确率约为 80%。结合两个分类器的结果,我们预测有 583 个 AGN 型候选,115 个脉冲星型候选,154 个其他类型 每个步骤的准确性和每个分类器给出的非关联源的类别。第一步得到的准确率约为95%;在第二步中,获得的整体准确率约为 80%。结合两个分类器的结果,我们预测有 583 个 AGN 型候选,115 个脉冲星型候选,154 个其他类型γ射线候选和484种不确定类型。

更新日期:2021-02-02
down
wechat
bug