论文标题
Fermi Lat目录中不确定类型的Blazars准确分类的混合方法
A Hybrid method of accurate classification for Blazars Of Uncertain Type in Fermi LAT Catalogs
论文作者
论文摘要
费米非相关来源的分类方面取得了重大进展，导致发现了越来越多的大麻。光谱可有效地将大麻片分为两组，例如BL LAC和平光无线电类星体（FSRQ）。然而，没有光谱信息的非确切分类，即不确定类型（BCUS）的烈酒，仍然是一个重大挑战。在本文中，我们介绍了主要成分分析（PCA）和机器学习混合Blazars分类方法。该方法基于Fermi LAT 3FGL目录的数据，首先使用PCA提取BCUS的主要功能，然后使用机器学习算法进一步对BCU进行分类。实验结果表明，PCA算法的使用显着改善了分类。更重要的是，与费米LAT 4FGL目录进行了比较，该目录包含Fermi-LAT 3FGL目录中这些BCU的光谱分类，这表明研究中提出的分类方法表现出比当前确定的方法更高的精度。具体而言，正确分类了171个BL LAC中的151个和24个FSRQ中的19个。
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.
