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SPACE WARPS - I. Crowdsourcing the discovery of gravitational lenses


Marshall, P J; Verma, A; More, A; Davis, C P; More, S; Kapadia, A; Parrish, M; Snyder, C; Wilcox, J; Baeten, E; Macmillan, C; Cornen, C; Baumer, M; Simpson, E; Lintott, C J; Miller, D; Paget, E; Simpson, R; Smith, A M; Ku ng, R; Saha, P; Collett, T E (2016). SPACE WARPS - I. Crowdsourcing the discovery of gravitational lenses. Monthly Notices of the Royal Astronomical Society, 455(2):1171-1190.

Abstract

We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowdsourced 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 deg^2 of Canada–France–Hawaii Telescope Legacy Survey imaging into some 430 000 overlapping 82 by 82 arcsec 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 eight 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 per cent complete and 30 per cent pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the Space Warps 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 d.

Abstract

We describe Space Warps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowdsourced 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 deg^2 of Canada–France–Hawaii Telescope Legacy Survey imaging into some 430 000 overlapping 82 by 82 arcsec 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 eight 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 per cent complete and 30 per cent pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the Space Warps 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 d.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Physics Institute
Dewey Decimal Classification:530 Physics
Language:English
Date:2016
Deposited On:29 Dec 2016 10:19
Last Modified:29 Dec 2016 10:31
Publisher:Oxford University Press
ISSN:0035-8711
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/mnras/stv2009
Related URLs:http://www.zora.uzh.ch/129781/

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