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arXiv 1209.4359astro-ph.HE
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通过机器学习算法的Gamma-Ray活动星系核类型

Gamma-Ray Active Galactic Nucleus Type through Machine-Learning Algorithms

T. Hassan, N. Mirabal, J. L. Contreras, I. Oya

摘要:

费米伽马射线太空望远镜正在制作迄今为止最详细的伽马射线天空清单。尽管取得了巨大的成就,但在第二个费米LAT目录(2FGL)中,大约25%的费米河外源被列为不确定类型的活动星系核(AGN)。通常,这些都是怀疑耀变体候选人没有一个决定性的光谱或缺乏光谱观测。在这里,我们探索使用机器学习算法-随机森林和支持向量机-预测特定活动星系核子类的基础上观察到的伽马射线光谱特性。在对2FGL的活动星系核进行训练和测试后,我们发现269个不确定类型的活动星系核中有235个具有与伽马射线BL Lac和平谱射电类星体兼容的性质,准确率为85%。此外,将我们的结果与Massaro等人(2012)的红外颜色空间后所做的类别预测进行直接比较,结果表明,对于54个重叠来源,一致率超过五分之四,提供了独立的交叉验证。这些结果可以帮助定制后续的光谱计划,并为未来使用地基切伦科夫望远镜的定点调查提供信息。

英文摘要:

The Fermi Gamma-ray Space Telescope is producing the most detailed inventory of the gamma-ray sky to date. Despite tremendous achievements approximately 25% of all Fermi extragalactic sources in the Second Fermi LAT Catalogue (2FGL) are listed as active galactic nuclei (AGN) of uncertain type. Typically, these are suspected blazar candidates without a conclusive optical spectrum or lacking spectroscopic observations. Here, we explore the use of machine-learning algorithms - Random Forests and Support Vector Machines - to predict specific AGN subclass based on observed gamma-ray spectral properties. After training and testing on identified/associated AGN from the 2FGL we find that 235 out of 269 AGN of uncertain type have properties compatible with gamma-ray BL Lacs and flat-spectrum radio quasars with accuracy rates of 85%. Additionally, direct comparison of our results with class predictions made after following the infrared colour-colour space of Massaro et al. (2012) show that the agreement rate is over four-fifths for 54 overlapping sources, providing independent cross validation. These results can help tailor follow-up spectroscopic programs and inform future pointed surveys with ground-based Cherenkov telescopes.

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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