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

TL;DR

This study evaluates how well human experts can identify strong gravitational lenses in survey images, highlighting their strengths, limitations, and the benefits of team-based classification for reliable candidate selection.

Contribution

It demonstrates the effectiveness and limitations of human visual inspection in gravitational lens detection and suggests team classification improves reliability.

Findings

Experts excel at identifying bright, well-resolved Einstein rings.

Detection drops for faint arcs and small Einstein radii.

Team of 6+ classifiers reduces performance variability.

Abstract

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$\%$ 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 unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst 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.