We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labelled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign, and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: https://github.com/georgestein/ssl-legacysurvey.
翻译:我们利用自我监督的演示学习,从暗能量光谱仪器遗留成像调查的数据发布9中7 600万个星系图像中提取信息。 我们首先针对识别新的强重力透镜候选人,建立了一个快速相似的搜索工具,以发现新的强力透镜,仅给出一个有标签的例子。 然后我们展示如何在自我监督的演示上培训一个简单的线性分类器,只需在CPU上花几分钟时间,就可以以极有效率的方式自动对强力透镜进行分类。 我们展示了1 192个新的强势透镜候选人,我们通过一个简短的视觉识别运动找到了这些候选人,并发布了一个基于网络的交互式类似搜索工具和顶级网络预测,以便利众包快速发现更多的强大重力透镜和其他稀有物体:https://github.com/georgstein/sl-legacysurveysurvey。