Smart Source Detection Framework for Lobster Eye Telescopes

doi:10.3847/1538-4365/ae092b

2025-11-10

DOI：10.3847/1538-4365/ae092b

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龙虾眼望远镜旨在捕捉来自广阔视场范围内天体的X射线辐射。然而，这些望远镜独特的光学系统导致探测器平面上形成十字形图案，从而增加了天体检测的复杂性。虽然机器学习推动了相关算法的发展，但实际应用仍面临观测条件变化等挑战，通常需要手动调整算法参数。仅依赖二维形态特征进一步限制了检测效率，因为能量分布和时变特性未被考虑。本文介绍了一种基于深度神经网络的创新源检测框架，提出三项进展：首先，采用Swin Transformer高效检测亮源；其次，利用神经网络从整合空间与能量信息的3D数据立方体中识别普通源；第三，在3D框架内设置自适应神经网络分支，自动调整阈值以分类真实源，适应观测条件变化。在模拟数据上的应用显示，该框架对亮度超过0.5 mCrab的目标实现了超过95%的精度和超过60%的召回率。在“爱因斯坦探针”搭载的广域X射线望远镜13,469张实际观测图像上的测试表明，与当前数据处理流程相比，准确率从79%提升至82%，检测到的源数量增加了27%。该源检测框架及其在选定天区生成的星表已向社区公开，以供进一步应用。

期刊

The Astrophysical Journal Supplement Series

IF:

论文数:

139

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机构

college of physics and optoelectronic engineering, taiyuan university of technology, taiyuan, 030024, people’s republic of china; robinmartin20@gmail.com, liys9378@gmail.com

学者数:

论文数: 1

被引数: 0

national astronomical observatories, beijing, 100101, people’s republic of china

学者数:

论文数: 1

被引数: 0

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