基于深度学习的费米-LAT未证认源中脉冲星候选体的分类与分析
Deep Learning-Based Classification and Analysis of Pulsar Candidates in Fermi-LAT Unassociated Sources
AI总结：
本研究开发了TabularResCNN深度学习框架，利用光谱与变异性特征对费米-LAT未证认源分类，识别出202个高置信度脉冲星候选体，增加脉冲星数量超60%，还验证了5个FAST近期证认的脉冲星。
AI中文摘要：
大视场望远镜（LAT）彻底改变了人类对高能天体的认知，但第四版费米-LAT源表（4FGL）中约三分之一的源仍未得到证认。传统机器学习方法如决策树，常将光谱特征视为独立表格条目，忽略了能谱分布（SED）固有的序列拓扑信息。本研究旨在利用未证认费米-LAT源的光谱本征形态与变异性特征进行分类，避免使用银道坐标作为训练特征，核心目标是生成高置信度脉冲星（PSR）候选体列表，并进一步区分年轻脉冲星（YPs）与毫秒脉冲星（MSPs）。我们开发了一种基于一维卷积神经网络（1D-CNN）的分层深度学习框架，命名为TabularResCNN，该架构处理4FGL源表的光谱数据，使模型能够学习光谱形态。分类分为两个阶段：首先区分活动星系核（AGNs）与脉冲星，随后将脉冲星分为年轻脉冲星与毫秒脉冲星。我们采用代价敏感学习策略处理类别不平衡问题，并利用梯度类激活映射（Grad-CAM）技术确保模型决策的物理解释性。将该框架应用于2563个未证认源，我们识别出1136个活动星系核和202个高置信度脉冲星候选体（166个年轻脉冲星、36个毫秒脉冲星），使脉冲星数量增加了60%以上。这些候选体呈现出强天体物理一致性：年轻脉冲星局限于银道面，毫秒脉冲星垂直分布更宽，活动星系核呈各向同性。此外，我们识别出5个被五百米口径球面射电望远镜（FAST）近期证认的脉冲星。所提出的1D-CNN框架基于本征光谱与时间特性分离脉冲星候选体，最大限度减少了空间偏差。
英文摘要：
The Large Area Telescope (LAT) has revolutionized our understanding of the high-energy sky, yet approximately one-third of the sources in the Fourth Fermi-LAT Source Catalog (4FGL) remain unassociated. Conventional machine learning, such as Decision Trees, often treat spectral features as independent tabular entries, neglecting the sequential topological information inherent in the Spectral Energy Distribution (SED). We aim to classify unassociated Fermi-LAT sources by exploiting the intrinsic shape of their spectra and variability features, avoiding the use of galactic coordinates as training features. Our primary objective is to generate a high-confidence list of PSRs candidates, further distinguishing between Young Pulsars (YPs) and Millisecond Pulsars (MSPs). We developed a hierarchical deep learning framework based on a 1D Convolutional Neural Network (1D-CNN), named TabularResCNN. This architecture treats the spectral data from the 4FGL catalog, allowing the model spectral shape. The classification is performed in two stages: first discriminating between AGNs and PSRs, and subsequently categorizing PSRs into YPs and MSPs. We implemented a cost-sensitive learning strategy to handle class imbalance and utilized Grad-CAM techniques to ensure the physical interpretability of the model's decisions. Applying this framework to 2563 unassociated sources, we identified 1136 AGNs and 202 high-confidence PSR candidates (166 YPs, 36 MSPs), increasing the pulsar population by more than 60%. They exhibit strong astrophysical consistency: YPs are confined to the Galactic plane, MSPs show a broader vertical distribution, and AGNs are isotropic. Furthermore, we identified 5 out of 5 PSRs recently confirmed by FAST. The proposed 1D-CNN framework isolates PSRs candidates based on intrinsic spectral and temporal properties, minimizing spatial bias.
