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arXiv 1602.00385astro-ph.HE
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使用机器学习技术对来自3FGL目录的Fermi LAT Gamma-ray源的分类和排序

Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques

P. M. Saz Parkinson, H. Xu, P. L. H. Yu, D. Salvetti, M. Marelli, A. D. Falcone

摘要:

我们应用一些统计和机器学习技术来分类和排名的伽马射线源从第三费米大面积望远镜(LAT)源目录(3FGL),根据他们的可能性落入两个主要类别的伽马射线发射器:星系(PSR)或活动星系核(AGN)。使用1904个已被识别/与AGN(1738)和PSR(166)相关的3FGL源,我们训练(使用70%的样本)和测试(使用30%)我们的算法,发现最好的整体精度(>96%)是用随机森林(RF)技术获得的,而使用逻辑回归(LR)算法的结果只有轻微的精度较低。我们将相同的技术应用于142个已知伽马射线双星的子样本,将它们分为两个主要的子类别:年轻的(YNG)和毫秒双星(MSP)。再次,RF算法具有最佳的整体准确度(~90%),而增强的LR分析紧随其后。我们应用我们的两个最好的模型(RF和LR)的整个3FGL目录,提供{ \it unassociated}源的可能性质的预测,包括脉冲星(YNG或MSP)的可能类型。我们还使用我们的预测来阐明一些伽马射线源的可能性质与已知的协会(如二进制,SNR/PWN)。最后,我们提供了一些脉冲星候选者的合理X射线对应物的列表,使用Swift,Chandra和XMM获得。我们的研究结果将对各种波长的深入后续搜索(例如脉冲星)以及更广泛的人群研究都有意义。

英文摘要:

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei (AGN). Using 1904 3FGL sources that have been identified/associated with AGN (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (~90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of {\it unassociated} sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g. binaries, SNR/PWN). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest for both in-depth follow-up searches (e.g. pulsar) at various wavelengths, as well as for broader population studies.

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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