Space Warps: I. Crowd-sourcing the Discovery of Gravitational Lenses

TL;DR

Space Warps is a crowd-sourced visual inspection system that efficiently discovers gravitational lenses with high completeness and purity, leveraging volunteer classifications and real-time feedback to analyze large astronomical datasets.

Contribution

This paper introduces a novel crowd-sourcing approach for gravitational lens discovery, combining real-time feedback and probabilistic modeling to improve detection accuracy.

Findings

Processed 160 sq. degrees of imaging data with 37,000 volunteers.

Reduced initial candidates from 430,000 tiles to 3,381 lens candidates.

Achieved over 90% completeness and 30% purity in the lens sample.

Abstract

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.