Mining for Strong Gravitational Lenses with Self-supervised Learning

TL;DR

This paper introduces a self-supervised learning approach to efficiently identify strong gravitational lens candidates from a vast galaxy image dataset, significantly accelerating the discovery process.

Contribution

It develops a self-supervised representation learning method and a similarity search tool for rapid, scalable identification of gravitational lenses with minimal labeled data.

Findings

Identified 1192 new strong lens candidates.

Created a similarity search tool for galaxy images.

Demonstrated efficient classification with minimal CPU time.

Abstract

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.