In this work, Machine Learning (ML) methods are used to efficiently identify the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the unassociated sources; 2) to identify BCUs into BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs). Two dimensional reduction methods are presented to decrease computational complexity, where Random Forest (RF), Multilayer Perceptron (MLP) and Generative Adversarial Nets (GAN) are trained as individual models. In order to achieve better performance, the ensemble technique is further explored. It is also demonstrated that grid search method is of help to choose the hyper-parameters of models and decide the final predictor, by which we have identified 748 AGNs out of 1010 unassociated sources, with an accuracy of 97.04%. Within the 573 BCUs, 326 have been identified as BL Lacs and 247 as FSRQs, with an accuracy of 92.13%.
@article{arxiv.2005.03546,
title = {Efficient Fermi Source Identification with Machine Learning Methods},
author = {Hubing Xiao and Haitao Cao and Junhui Fan and Denise Costantin and Gaoyong Luo and Zhiyuan Pei},
journal= {arXiv preprint arXiv:2005.03546},
year = {2020}
}
Comments
Accepted for publication in Astronomy and Computing