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

Journal article

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 machine learning (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 ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, 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 expert assessments. The top 800 ViT-scored images include ∼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.

Authors: González, J; Holloway, P; Collett, T; Verma, A; Bechtol, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Astronomical Society
• Journal: The Astrophysical Journal
• Volume: 1002
• Issue: 2
• Article number: 116
• Publication date: 2026-04-30
• Acceptance date: 2026-01-07
• DOI: 10.3847/1538-4357/ae450c
• EISSN: 1538-4357
• ISSN: 0004637X, 0004-637X
• Language: English
• Keywords: Neural networks; Strong gravitational lensing