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

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arxiv 2501.15679 v2 pith:P2OZOQ54 submitted 2025-01-26 astro-ph.GA

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

J. Gonzalez , P. Holloway , T. Collett , A. Verma , K. Bechtol , P. Marshall , A. More , J. Acevedo Barroso
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G. Cartwright M. Martinez T. Li K. Rojas S. Schuldt S. Birrer H. T. Diehl R. Morgan A. Drlica-Wagner J. H. O'Donnell E. Zaborowski B. Nord E. M. Baeten L. C. Johnson C. Macmillan A. Roodman A. Pieres A. R. Walker A. A. Plazas Malag\'on A. Carnero Rosell B. Santiago B. Flaugher D. Gruen D. Brooks D. L. Burke D. J. James D. Sanchez Cid D. L. Hollowood D. L. Tucker E. Buckley-Geer E. Gaztanaga E. Suchyta E. Sanchez G. Gutierrez G. Giannini G. Tarle I. Sevilla-Noarbe J. L. Marshall J. Carretero J. Frieman J. De Vicente J. Garc\'ia-Bellido J. Mena-Fern\'andez J. Myles K. Honscheid K. Kuehn M. Lima M. E. S. Pereira M. Smith M. Aguena N. Weaverdyck O. Lahav P. Doel R. Miquel R. A. Gruendl R. Cawthon S. R. Hinton S. S. Allam S. Desai S. Samuroff S. Everett S. Lee T. M. Davis T. M. C. Abbott V. Vikram
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classification astro-ph.GA
keywords lensescandidateslenslearningmachinearounddarkdefinite
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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 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.

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  1. Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems

    astro-ph.IM 2026-04 conditional novelty 6.0

    A vision transformer classifier trained on simulated and real Euclid data recovers all known strong lenses in test sets and finds 8 Grade A plus 26 Grade B new candidates in the Q1 data.