论文标题
使用机器学习方法的有效的费米来源识别
Efficient Fermi Source Identification with Machine Learning Methods
论文作者
论文摘要
在这项工作中，机器学习（ML）方法用于有效地识别Fermi-Lat第三源目录中的不确定类型（BCUS）的非相关源和Blazar候选者（3FGL）。目的是双重的：1）将主动银河核（AGN）与其他相关来源中的其他（非AGNS）区分开； 2）将BCUS识别为Bl lacertae对象（BL LACS）或平光无线电类星体（FSRQ）。提出了二维还原方法以降低计算复杂性，其中随机森林（RF），多层感知器（MLP）和生成对抗网（GAN）被训练为单个模型。为了取得更好的性能，进一步探索了整体技术。还证明，网格搜索方法有助于选择模型的超参数并确定最终预测因子，通过该预测指标，我们已经确定了1010个非相关来源中的748个AGN，精度为97.04％。在573 BCU中，已将326个被鉴定为BL LAC，而247为FSRQ，精度为92.13％。
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%.
