利用U-Net和ResNet识别KiDS数据集中被前景光遮蔽的引力透镜
Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets
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中文总结 AI 辅助
针对前景光遮蔽导致引力透镜难以探测的问题，提出结合U-Net和ResNet的混合搜索方法，在KiDS数据中识别出大量候选体，显著提升了搜索完备性。
中文摘要 AI 辅助
*背景.* 许多透镜图像常常被中央星系的前景光所遮蔽，使其难以被探测。*目标.* 为了解决以往透镜搜索工作的局限性，特别是针对具有较小$R_E$或微弱透镜图像的样本，我们开发了一个复合卷积神经网络框架，利用U-Net和ResNet架构进行特征提取和分类。*方法.* 我们提出了一种结合U-Net和ResNet架构的混合搜索方法，以增强对前景星系遮蔽透镜的探测。我们的方法包含两个主要阶段：首先，U-Net模型将前景星系光与潜在的透镜信号分离，生成突出透镜特征的残差图像。接下来，ResNet模块对这些残差图像进行二分类以检测透镜信号。*结果.* 我们使用真实观测数据评估了混合搜索方法的有效性，在置信度阈值为0.6时，实现了71.5%的召回率和4.5%的假阳性率。将该方法应用于Kilo-Degree巡天第四批数据发布中的超过638,398个星系样本，并进行彻底检查后，我们识别出88个A类、322个B类和1,758个C类候选体。*结论.* 这种混合方法显著提高了现有强引力透镜搜索的完备性，并显示出在未来天文巡天中应用的巨大潜力。
英文摘要
*Context.* Many lensing images are often obscured by foreground light from the central galaxies, making them challenging to detect. *Aims.* To address the limitations of previous lens search efforts, particularly for samples with smaller $R_E$ or faint lensed images, we developed a composite convolutional neural network framework that utilizes both U-Net and ResNet architectures for feature extraction and classification. *Methods.* We propose a hybrid search method that combines U-Net and ResNet architectures to enhance the detection of foreground galaxy-obscured lenses. Our approach consists of two main stages: first, the U-Net model separates the foreground galaxy light from potential lensing signals, creating residual images that highlight the lensing features. Next, the ResNet module performs binary classification on these residual images to detect lensing signals. *Results.* We evaluated the hybrid search method with real observational data to demonstrate its effectiveness, achieving a recall of 71.5% and a 4.5% false positive rate at a confidence threshold of 0.6. Applying this method to over 638,398 galaxy samples from the Kilo-Degree Survey Data Release 4 and conducting thorough inspections, we identify 88 Class A, 322 Class B, and 1,758 Class C candidates. *Conclusions.* This hybrid approach significantly enhances the completeness of existing strong gravitational lensing searches and shows great potential for improving future astronomical surveys.
发表机构
- Wuhan University（武汉大学）
- Zhengzhou University（郑州大学）
- Nanchang University（南昌大学）
- INAF – Osservatorio Astronomico di Capodimonte（意大利国家天体物理研究所卡波迪蒙特天文台）
- University Federico II（那不勒斯费德里科二世大学）
- Sun Yat-sen University（中山大学）
- Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）
- Xi’an Jiaotong-Liverpool University（西交利物浦大学）
- Beijing Normal University（北京师范大学）
机构由 AI 辅助整理，请以论文原文为准。
