Classification of Fermi BCUs Using Machine Learning

Abstract

The Fermi Large Area Telescope (LAT) has detected 6659 γ-ray sources in the incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi-LAT catalog of γ-ray sources and 3743 of them are blazars, including 1517 blazar candidates of uncertain type (BCUs). Blazars are generally classified by properties of emission lines into BL Lac objects and flat spectrum radio quasars (FSRQs). However, BCUs are difficult to classify because of the lack of spectrum. In this work we apply five different machine-learning algorithms (K-nearest neighbors, logistic regression, support vector machine, random forest, CatBoost) to evaluate the classification of 1517 BCUs based on the observational data of 4FGL-DR3. The results indicate that the use of recursive feature elimination cross-validation can effectively improve the accuracy of models and reduce computation time. We use our models to predict the BCUs from 4FGL-DR3 and the results of the overlapping of the five models are as follows: 811 BL Lac objects, 397 FSRQs, and 309 BCUs.

A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars

“…In order to effectively separate these two classes, we employed a kind of machine-learning (ML) method to establish a dividing line. Recently, ML methods, such as support vector machine (SVM), artificial neural networks, K-nearest neighbors, etc., have been widely used in astronomy; see Kang et al (2019), Kovačević et al (2019), Xu et al (2020), Zhu et al (2021), Xiao et al (2022bXiao et al ( , 2022cXiao et al ( , 2023, and Zhu et al (2023). The SVM model can easily handle both linear and nonlinear classification problems by choosing different kernel functions.…”