使用能量和时间谱进行深度学习的Fermi-LAT源分类
Classification of Fermi-LAT sources with deep learning using energy and time spectra
摘要：
尽管费米-LAT探测到的伽马射线源越来越多，但每次调查中仍有约三分之一的源类型不确定。我们提出了一种新的深度神经网络方法，用于对使用10年数据获得的费米-LAT目录（4FGL-DR 2）的最新版本中未识别或未关联的伽马射线源进行分类。与以前的工作相比，我们的方法直接使用光子能谱和时间序列的测量值作为分类的输入，而不是特定的人工特征。密集的神经网络，并首次在伽马射线源分类的背景下，递归神经网络，进行了深入研究。我们专注于河外源之间的分离，即活动星系核和银河星系核，并进一步将星系核分类为年轻星系核和毫秒星系核。我们的神经网络架构提供了强大的分类器，其性能与以前基于人工特征的分析相当。我们的基准神经网络预测，在4FGL-DR 2中不确定类型的源中，有1050个是活动星系核，78个是星系核，这两类都遵循预期的天空分布和可变曲率平面中的聚类。我们调查样本选择偏差的问题，通过测试我们的架构对交叉匹配测试数据集使用旧目录，并提出了一个功能选择算法，使用自动编码器。我们的神经网络标记的高置信度候选源列表为进一步的多波长观测提供了一组目标，以确定其性质。我们开发的深度神经网络架构可以很容易地扩展到包括特定的特征，以及来自不同仪器的源光子能量和时间谱的多波长数据。
英文摘要：
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.
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