Mining for Strong Gravitational Lenses with Self-supervised Learning
This work addresses the challenge of finding rare strong gravitational lenses in large astronomical datasets, which is incremental as it builds on existing self-supervised methods for improved efficiency.
The researchers tackled the problem of identifying strong gravitational lens candidates by applying self-supervised learning to 76 million galaxy images, resulting in the discovery of 1192 new candidates and the creation of a rapid similarity search tool.
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labelled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign, and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: https://github.com/georgestein/ssl-legacysurvey.