Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

Abstract: We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our ML architecture and adopt Interactive Machine Learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces 236 million DES cutout images to 22,564 targets of interest, including around 85% of previously reported galaxy-galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out approximately 90% as false positives. Of the remaining 2,618 candidates, 149 were expert-classified as 'definite' lenses and 516 as 'probable' lenses, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy-galaxy lenses, consistently assigning high scores to candidates with high confidence. The top 800 ViT-scored images include around 100 of our `definite' lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.