Fermi LAT星表中不确定型Blazar精确分类的混合方法
A Hybrid method of accurate classification for Blazars Of Uncertain Type in Fermi LAT Catalogs
摘要：
随着费米非关联源分类研究的不断深入，blazar星的发现也越来越多，利用光谱可以将blazar星分为BL Lac和平坦谱射电类星体（Flat spectrum radioquasar，FSRQ）两大类。然而，在没有光谱信息的情况下，不确定类型的耀变体（BCU）仍然是一个重大挑战。本文提出了一种主成分分析和机器学习相结合的blazar分类方法。该方法基于Fermi LAT 3FGL星表的数据，首先使用PCA提取BCU的主要特征，然后使用机器学习算法对BCU进行进一步分类。实验结果表明，使用PCA算法显著提高了分类效果.更重要的是，与Fermi-LAT 3FGL星表中包含这些BCU的光谱分类的Fermi-LAT 4FGL星表进行比较，表明该研究中提出的分类方法比目前建立的方法具有更高的准确性;具体而言，171个BL Lac中的151个和24个FSRQ中的19个被正确分类。
英文摘要：
Significant progress in the classification of Fermi unassociated sources , has led to an increasing number of blazars are being found. The optical spectrum is effectively used to classify the blazars into two groups such as BL Lacs and flat spectrum radio quasars (FSRQs). However, the accurate classification of the blazars without optical spectrum information, i.e., blazars of uncertain type (BCUs), remains a significant challenge. In this paper, we present a principal component analysis (PCA) and machine learning hybrid blazars classification method. The method, based on the data from Fermi LAT 3FGL Catalog, first used the PCA to extract the primary features of the BCUs and then used a machine learning algorithm to further classify the BCUs. Experimental results indicate that the that the use of PCA algorithms significantly improved the classification. More importantly, comparison with the Fermi LAT 4FGL Catalog, which contains the spectral classification of those BCUs in the Fermi-LAT 3FGL Catalog, reveals that the proposed classification method in the study exhibits higher accuracy than currently established methods; specifically, 151 out of 171 BL Lacs and 19 out of 24 FSRQs are correctly classified.
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