[论文解读] Research and characterisation of blazar candidates among the Fermi/LAT 3FGL catalogue using multivariate classifications

Julien Lefaucheur, Santiago Pita|HAL (Le Centre pour la Communication Scientifique Directe)|Mar 6, 2017

Astrophysics and Cosmic Phenomena被引用 4

一句话总结

本研究使用多变量分类器——提升决策树与多层感知器神经网络——对费米/LAT 3FGL星表数据进行分析，从无关联源中识别出595个新的耀变体候选体，并对候选体及BCUs中的509个BL Lacs和295个FSRQs进行分类。该方法在高银纬候选体中实现了极低的预期错误关联率（约425个中9个），显著扩充了用于星系际背景光与耀变体序列研究的γ射线耀变体样本。

ABSTRACT

In the recently published 3FGL catalogue, the Fermi/LAT collaboration reports the detection of $γ$-ray emission from 3034 sources obtained after four years of observations. The nature of 1010 of those sources is unknown, whereas 2023 have well-identified counterparts in other wavelengths. Most of the associated sources are labelled as blazars (1717/2023), but the BL Lacs or FSRQ nature of 573 of these blazars is still undetermined. The aim of this study was two-fold. First, to significantly increase the number of blazar candidates from a search among the large number of Fermi/LAT 3FGL unassociated sources (case A). Second, to determine the BL Lac or FSRQ nature of the blazar candidates, including those determined as such in this work and the blazar candidates of uncertain type (BCU) that are already present in the 3FGL catalogue (case B). For this purpose, multivariate classifiers were trained using samples of labelled sources with no caution flag from the 3FGL catalogue and carefully chosen discriminant parameters. The decisions of the classifiers were combined in order to obtain a high level of source identification along with well controlled numbers of expected false associations. Specifically for case A, dedicated classifications were generated for high and low galactic latitude sources, in addition, the application of classifiers to samples of sources with caution flag was considered separately, and specific performance metrics were estimated. We obtained a sample of 595 blazar candidates among the unassociated sources of the 3FGL catalogue. We also obtained a sample of 509 BL Lacs and 295 FSRQs from the blazar candidates cited above and the BCUs of the 3FGL catalogue. The number of expected false associations is given for different samples of candidates. It is, in particular, notably low ($\sim$9/425) for the sample of high-latitude blazar candidates from case A.

研究动机与目标

• 显著增加费米/LAT 3FGL中1010个性质未知的无关联源中耀变体候选体的数量。
• 确定耀变体候选体（包括3FGL星表中类型不确定的BCUs）的BL Lacs或FSRQ分类。
• 通过稳健的多变量分类技术与可控误差率，降低错误关联率。
• 为星系际背景光与耀变体序列研究提供更精确的γ射线耀变体群体统计表征。

提出的方法

• 在无警告标志的3FGL源上训练提升决策树与多层感知器神经网络，使用精心挑选的判别参数。
• 分别对高银纬（|b| > 10°）和低银纬（|b| ≤ 10°）的无关联源应用分类器，以考虑银河平面区域的密集源影响。
• 通过集成投票与性能阈值设定，结合分类器决策，以最大化识别置信度并最小化错误关联。
• 对带有警告标志的源单独评估分类器性能，估算不同候选体样本的预期假阳性率。
• 使用TMVA（多变量分析工具包）进行分类器训练，使用STILTS进行交叉匹配与数据处理。
• 通过光谱观测与先前研究验证结果，以评估其一致性与可靠性。

实验结果

研究问题

• RQ1 利用多变量分类方法，可在费米/LAT 3FGL无关联源中识别出多少个新的耀变体候选体？
• RQ2 在识别出的耀变体候选体样本中，特别是高银纬区域，预期的错误关联率是多少？
• RQ3 多变量分类器在新识别的候选体及BCUs中，对BL Lacs与FSRQs类型的区分能力如何？
• RQ4 结果与独立的光谱后续观测及先前分类研究的一致性如何？
• RQ5 与仅基于变异性参数的分类器相比，能量谱与变异性参数的结合能否更有效地分离BL Lacs与FSRQs？

主要发现

• 本研究在费米/LAT 3FGL无关联源中识别出595个新的耀变体候选体，高银纬样本的预期错误关联率极低，约为425个中9个。
• 通过新候选体与BCUs的结合，共识别出509个BL Lacs与295个FSRQs，显著扩展了已知的γ射线耀变体群体。
• BL Lacs分类器的真正例率约为84%，假正例率约为5%，优于以往仅基于变异性参数的方法。
• 对于FSRQs，该方法实现了约69%的真正例率与约12%的假正例率，且在光谱参数上表现优于仅依赖变异性参数的方法。
• 在一项光谱观测计划中，31个源中有25个被分类器确认为BL Lacs或FSRQs，仅2个被错误分类，表明与观测验证具有高度一致性。
• 当引入光谱参数时，BL Lacs与FSRQs在变异性空间中的重叠区域得到更好分辨，凸显了光谱参数在分类中的关键作用。