The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25 per cent of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabelled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, while arcs with g-band signal to noise less than & SIM;25 or Einstein radii less than & SIM;1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.

Automated summary

We investigated the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey-like imaging. A total of 55 participants completed more than 25% of the project and classified 1489 images, including lens simulations, real lenses, non-lens examples, and unlabelled data. Experts were good at identifying bright, well-resolved Einstein rings, but struggled with fainter arcs and smaller Einstein radii. Individual classifiers showed variation in performance, but experience, confidence, and academic position did not seem to have an impact. These variations could be mitigated with a team of 6 or more independent classifiers. The results give confidence that humans can reliably prune lens candidates, providing complete samples for follow-up studies.