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

Authors: J. González, P. Holloway, T. Collett, A. Verma, K. Bechtol, P. Marshall, A. More, J. Acevedo Barroso, 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, T. M. C. Abbott, M. Aguena, S. S. Allam, D. Brooks, E. Buckley-Geer, D. L. Burke, A. Carnero Rosell, J. Carretero, R. Cawthon, T. M. Davis, J. De Vicente, S. Desai, P. Doel, S. Everett, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, O. Lahav, S. Lee, M. Lima, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. Myles, M. E. S. Pereira, A. Pieres, A. A. Plazas Malagón, A. Roodman, S. Samuroff, E. Sanchez, D. Sanchez Cid, B. Santiago, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, D. L. Tucker, V. Vikram, A. R. Walker, N. Weaverdyck

J. González et al 2026 The Astrophysical Journal 1002 .

Provider: AAS Journals

Caption: Figure 5.

Architecture of the Vision Transformer (ViT; A. Dosovitskiy et al. 2021). The input image is divided into fixed-size image sections, which are linearly embedded and combined with position embeddings. These embeddings are then processed by a Transformer Encoder, which applies multiple self-attention mechanisms. The output is finally fed into a classification head to predict the input image’s class based on the training categories.

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