论文标题
使用能量和时间光谱对费米 - 拉特源分类
Classification of Fermi-LAT sources with deep learning using energy and time spectra
论文作者
论文摘要
尽管费米 - 拉特检测到的伽马射线源数量越来越大，但每个调查中约有三分之一的来源仍然是不确定的。我们提出了一种新的深神经网络方法，用于在Fermi-LAT目录的最后一个版本（4FGL-DR2）中对未识别或未相关的伽马射线来源进行分类，并获得了10年的数据。与以前的工作相反，我们的方法直接使用光子能谱和时间序列的测量结果作为分类的输入，而不是特定的，人工制作的特征。密集的神经网络，以及在伽马射线源分类复发性神经网络中首次研究。我们专注于外乳外源，即\ \活性银河核和银河脉冲星之间的分离，以及将脉冲星的进一步分类为年轻和毫秒的脉冲星。我们的神经网络体系结构提供了强大的分类器，其性能可与基于人工制作的功能进行的分析相媲美。我们的基准神经网络预测4FGL-DR2、1050中不确定类型的来源是活跃的银河系核，而78是银河脉冲星，两个类都遵循预期的天空分布，并且在变异性阳光平面中的聚类。我们通过使用较旧的目录来测试我们的架构对交叉匹配测试数据集的架构来研究样本选择偏差的问题，并使用自动编码器提出特征选择算法。我们由神经网络标记的高信任候选资源列表为进一步的多波长观测值提供了一组目标，以识别其性质。我们开发的深神经网络体系结构可以很容易地扩展到包含特定功能，以及来自不同仪器的源光子能量和时间光谱的多波长数据。
Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or unassociated gamma-ray sources in the last release of the Fermi-LAT catalogue (4FGL-DR2) obtained with 10 years of data. In contrast to previous work, our method directly uses the measurements of the photon energy spectrum and time series as input for the classification, instead of specific, human-crafted features. Dense neural networks, and for the first time in the context of gamma-ray source classification recurrent neural networks, are studied in depth. We focus on the separation between extragalactic sources, i.e.\ Active Galactic Nuclei, and Galactic pulsars, and on the further classification of pulsars into young and millisecond pulsars. Our neural network architectures provide powerful classifiers, with a performance that is comparable to previous analyses based on human-crafted features. Our benchmark neural network predicts that of the sources of uncertain type in the 4FGL-DR2, 1050 are Active Galactic Nuclei and 78 are Galactic pulsars, with both classes following the expected sky distribution and the clustering in the variability-curvature plane. We investigate the problem of sample selection bias by testing our architectures against a cross-match test data set using an older catalogue, and propose a feature selection algorithm using autoencoders. Our list of high-confidence candidate sources labelled by the neural networks provides a set of targets for further multiwavelength observations addressed to identify their nature. The deep neural network architectures we develop can be easily extended to include specific features, as well as multiwavelength data on the source photon energy and time spectra coming from different instruments.
