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arXiv 2102.07642astro-ph.HEastro-ph.IM
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构造概率Fermi-LAT星表的机器学习方法

Machine learning methods for constructing probabilistic Fermi-LAT catalogs

Aakash Bhat, Dmitry Malyshev

摘要:

源的分类是天文学中最重要的任务之一。在一个波段中检测到的源,例如使用伽马射线,在其他波段中可能有几个可能的关联,或者可能没有合理的关联候选。在这项工作中,我们的目标是确定在第三费米大面积望远镜(LAT)的点源目录(3FGL)和第四费米LAT数据发布2点源目录(4FGL-DR 2)使用两个类-恒星和活动星系核(AGN)-或三个类-恒星,活动星系核,和“其他”源的无关联源的概率分类。我们使用几种机器学习(ML)方法来确定费米LAT源的概率分类。我们评估结果对ML方法的元参数的依赖性,例如基于树的分类方法中的树的最大深度和神经网络中的神经元数量。我们确定的概率分类的3FGL和4FGL-DR 2目录中的关联和非关联源。我们通过比较3FGL中的无关联源的预测类别与4FGL-DR 2中存在此类关联的情况下的关联来交叉检查准确性。我们发现,在两个类的情况下,重要的是要纠正的存在,其他来源之间的不相关的,以现实地估计的数量的恒星和活动星系核。我们发现,三级分类,尽管不同类型的其他类中的源,具有类似的性能作为两级分类的可靠性图方面,在同一时间,它不需要调整,由于存在的其他来源之间的不相关的来源。我们展示了一个例子,使用的概率目录的人口研究,其中包括相关和不相关的来源。

英文摘要:

Classification of sources is one of the most important tasks in astronomy. Sources detected in one wavelength band, for example using gamma rays, may have several possible associations in other wavebands, or there may be no plausible association candidates. In this work we aim to determine the probabilistic classification of unassociated sources in the third Fermi Large Area Telescope (LAT) point source catalog (3FGL) and the fourth Fermi LAT data release 2 point source catalog (4FGL-DR2) using two classes - pulsars and active galactic nuclei (AGNs) - or three classes - pulsars, AGNs, and "OTHER" sources. We use several machine learning (ML) methods to determine a probabilistic classification of Fermi-LAT sources. We evaluate the dependence of results on the meta-parameters of the ML methods, such as the maximal depth of the trees in tree-based classification methods and the number of neurons in neural networks. We determine a probabilistic classification of both associated and unassociated sources in the 3FGL and 4FGL-DR2 catalogs. We cross-check the accuracy by comparing the predicted classes of unassociated sources in 3FGL with their associations in 4FGL-DR2 for cases where such associations exist. We find that in the two-class case it is important to correct for the presence of OTHER sources among the unassociated ones in order to realistically estimate the number of pulsars and AGNs. We find that the three-class classification, despite different types of sources in the OTHER class, has a similar performance as the two-class classification in terms of reliability diagrams and, at the same time, it does not require adjustment due to presence of the OTHER sources among the unassociated sources. We show an example of the use of the probabilistic catalogs for population studies, which include associated and unassociated sources.

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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