发表机构
University of Oxford; National Centre for Nuclear Research; University of the Western Cape（牛津大学; 国家核研究中心; 西开普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将Astronomaly:PROTEGE主动学习框架应用于KiDS数据，结合自监督学习，无需模拟数据即可发现强引力透镜，识别出140个高质量候选体，为下一代巡天提供了可行方案。
AI 中文摘要
强引力透镜（SGLs）是一类稀有系统，目前其发现主要依赖于在大型模拟数据集上训练的监督机器学习方法。我们首次将Astronomaly:PROTEGE应用于SGL发现，证明了人机协作的主动学习框架可在大型成像巡天中高效识别透镜，无需依赖模拟训练数据。我们选取了千平方度巡天（KiDS）DR4中的370万个明亮星系样本，使用在ImageNet数据集上预训练的卷积神经网络提取特征表示，随后通过自监督的Bootstrap Your Own Latent（BYOL）框架在KiDS数据上进行微调。在这些表示的嵌入空间中，Astronomaly的主动学习循环会迭代选择最具信息价值的系统供专家检查。经过多轮检查共3000个天体，得到34个高质量（A/B级）SGL候选体。基于这些系统在学习特征空间中占据相似区域，我们通过近邻相似度分析扩展了该样本。结合主动学习的发现，我们共识别出140个A/B级候选体和1000多个低置信度（C级）系统。在A/B级候选体中，81个为新发现，同时约22%的已知KiDS A/B级透镜被成功复现。这些结果证明了该方法对Euclid、Roman和Rubin的空间与时间遗产巡天等下一代巡天的巨大潜力。由于约60%的高质量候选体为新报道，该方法通过减少对模拟的依赖并实现多样化SGL群体的发现，对监督方法形成补充。
英文摘要
Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We present the first application of Astronomaly:PROTEGE to SGL discovery, demonstrating that a human-in-the-loop active learning framework can efficiently identify lenses in large imaging surveys without the need for simulated training data. We consider a sample of 3.7 million bright galaxies from the Kilo-Degree Survey (KiDS) DR4. Feature representations are extracted using a convolutional neural network pre-trained on the ImageNet dataset and subsequently fine-tuned on KiDS data using the self-supervised Bootstrap Your Own Latent (BYOL) framework. Within the embedding of these representations, the active learning loop of Astronomaly iteratively selects the most informative systems for expert inspection. A total of 3,000 objects are inspected across multiple rounds, yielding 34 high-quality (grade A/B) SGL candidates. On the basis that these systems occupy similar regions in the learned feature space, we expand this sample through nearest-neighbour similarity analysis. Including the active learning discoveries, we identify a total of 140 grade A/B candidates and more than 1,000 additional lower-confidence systems (grade C). Among the A/B candidates, 81 are newly identified, while approximately 22% of previously known grade A/B KiDS lenses are recovered. These results demonstrate strong potential for next-generation surveys such as Euclid, Roman, and Rubin's Legacy Survey of Space and Time. With approximately 60% of the high-quality candidates newly reported, this approach complements supervised methods by reducing reliance on simulations and enabling the discovery of a diverse population of SGLs.
CommentsAccepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)
