论文标题
通过人工神经网络的Fermi LAT 8年源目录的不确定类型的Blazar候选者的分类
Classification of Blazar Candidates of Uncertain Type from the Fermi LAT 8-Year Source Catalog with an Artificial Neural Network
论文作者
论文摘要
费米大面积望远镜（LAT）在运行的头8年中检测到了5000多个伽马射线。其中有3000多个是Blazars。大约60％的费米 - 拉特大麻被归类为bl lacertae对象（Bl lacs）或扁平频谱无线电类星体（FSRQ），而其余的则是不确定的类型。这项研究的目的是通过将基于人工神经网络的监督机器学习方法与已知的伽马射线源的属性进行比较，以使用基于人工神经网络的监督机器学习方法对那些不确定类型的吹奏进行分类。获得1329个不确定的Blazars中的每种概率是BL LAC或FSRQ。使用90％的精度度量，可以将801分类为BL LAC，为FSRQ 406，而122仍未分类。这种方法引起了人们的关注，因为它给出了不确定的大麻的快速初步分类。我们还探讨了不同选择的培训和测试样品如何影响分类并讨论网络输出的含义。
The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray sources in its first 8 years of operation. More than 3000 of them are blazars. About 60 per cent of the Fermi-LAT blazars are classified as BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs), while the rest remain of uncertain type. The goal of this study was to classify those blazars of uncertain type, using a supervised machine learning method based on an artificial neural network, by comparing their properties to those of known gamma-ray sources. Probabilities for each of 1329 uncertain blazars to be a BL Lac or FSRQ are obtained. Using 90 per cent precision metric, 801 can be classified as BL Lacs and 406 as FSRQs while 122 still remain unclassified. This approach is of interest because it gives a fast preliminary classification of uncertain blazars. We also explored how different selections of training and testing samples affect the classification and discuss the meaning of network outputs.
