用自监督学习识别天文影像中的低质量曝光，提升大规模巡天数据质检效率。
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
- 结合视觉变换器与kNN分类器的半监督方法
- 在低消光区发现780个问题曝光，验证准确率高
- 适合需要高效质检的大型天文巡天项目
随着天文成像巡天数据量急剧增长，传统依赖人工目视的图像异常检测方法已难以为继。本文提出一种基于机器学习的方法，用于检测大尺度成像巡天中的低质量曝光，聚焦于暗能量相机（DECam）在低消光区域（$E(B-V)<0.04$）的观测数据。所提半监督流程整合了通过自监督学习（SSL）训练的视觉变换器（ViT）与k近邻（kNN）分类器。模型使用少量标注的DECam观测数据进行训练与验证。对标记为“良好”和“不良”的图像在聚类空间中的分布分析表明，该方法能高效且准确地判断图像质量。将其应用于DECaLS数据发布11版的新处理图像，共识别出780个有问题的曝光，后续经人工目视确认。该方法高效且可迁移，为其他大型成像巡天提供了可扩展的质量控制方案。
原文摘要 · Abstract (English)
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., $E(B-V)<0.04$). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in ``good'' and ``bad'' categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。