Space Warps – I. Crowdsourcing the discovery of gravitational lenses
Space Warps – I. Crowdsourcing the discovery of gravitational lenses
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DOI：
10.1093/mnras/stv2009
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发表时间：
2015-04
影响因子：
4.8
通讯作者：
P. Marshall;A. Verma;A. More;C. Davis;S. More;A. Kapadia;M. Parrish;C. Snyder;J. Wilcox;E. Baeten;C. Macmillan;C. Cornen;M. Baumer;Edwin Simpson;C. Lintott;David Miller;E. Paget;R. Simpson;Arfon M. Smith;R. Kung;P. Saha;T. Collett;M. Tecza
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作者：
P. Marshall;A. Verma;A. More;C. Davis;S. More;A. Kapadia;M. Parrish;C. Snyder;J. Wilcox;E. Baeten;C. Macmillan;C. Cornen;M. Baumer;Edwin Simpson;C. Lintott;David Miller;E. Paget;R. Simpson;Arfon M. Smith;R. Kung;P. Saha;T. Collett;M. Tecza
我们描述了 SpaceWarps，这是一种新颖的引力透镜发现服务，可通过众包目视检查产生高纯度和完整性的样本。精心制作的彩色合成图像通过基于网络的分类界面向志愿者显示，该界面记录了他们对候选镜头特征位置的估计。模拟镜头的图像以及缺少镜头的真实图像以随机间隔插入到图像流中；该训练集用于向志愿者提供有关其表现的即时反馈，以及校准系统模型，该模型为分类图像包含镜头的概率提供动态更新。低概率系统定期从现场退役，将样本集中到一组候选镜片上。我们将 160 平方度的加拿大-法国-夏威夷望远镜遗产巡天 (CFHTLS) 成像分成约 430,000 个重叠的 82 x 82 角秒图块并将其显示在网站上，大约 37,000 名志愿者加入了我们，他们在 8 个月的时间里贡献了 1100 万个图像分类。第一阶段搜索将样本减少到 3381 张包含候选图像的图像；然后，根据我们对志愿者在训练图像上的表现的分析，这些样本将在第二阶段进行细化，以产生我们预计完整度超过 90%、纯度超过 30% 的样本。我们对 SpaceWarps 系统在广域调查时代的可扩展性进行了评论，根据我们的预测，105 名志愿者可以在 6 天内搜索 105 张图像。
We describe SpaceWarps, 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 webbased 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 105 images could be performed by a crowd of 105 volunteers in 6 days.