空间扭曲：I.引力透镜的发现
Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses
摘要：
我们描述了Space Warps，这是一种新型的引力透镜发现服务，通过众包的目视检查产生高纯度和完整性的样品。精心制作的彩色合成图像通过基于网络的分类界面显示给志愿者，该界面记录了他们对候选透镜特征位置的估计。模拟镜片的图像以及缺少镜片的实像以随机间隔插入图像流中;该训练集用于向志愿者提供关于其表现的即时反馈，以及校准系统的模型，该模型提供对分类图像包含镜片的概率的动态更新。低概率系统定期从现场退役，将样本集中到一组镜片候选者。在将160平方度的加拿大-法国-夏威夷望远镜遗产调查（CFHTLS）图像划分为约430，000个重叠的82 × 82角秒瓷砖并在网站上显示之后，我们加入了大约37，000名志愿者，他们在8个月的时间里贡献了1100万个图像分类。第一阶段的搜索将样本减少到3381张包含候选图像的图像;然后在第二阶段对这些图像进行优化，根据我们对志愿者在训练图像上的表现的分析，我们期望得到一个超过90%完整和30%纯度的样本。我们评论的扩展性的SpaceWarps系统的广域调查时代，根据我们的预测，搜索10个$^5$图像可以由一群10个$^5$志愿者在6天内进行。
英文摘要：
We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection. Carefully produced colour composite images are displayed to volunteers via a web- based classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 square degrees of Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging into some 430,000 overlapping 82 by 82 arcsecond tiles and displaying them on the site, we were joined by around 37,000 volunteers who contributed 11 million image classifications over the course of 8 months. This Stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in Stage 2 to yield a sample that we expect to be over 90% complete and 30% pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the SpaceWarps system to the wide field survey era, based on our projection that searches of 10$^5$ images could be performed by a crowd of 10$^5$ volunteers in 6 days.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
