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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 9.

A plot of the user skills, based on their performance on a training set. The user skill is defined as the fraction of correctly classified training subjects of each type, e.g the probability a user will classify a training subject as being a lens, given that it is indeed a lens, P(“Lens”∣Lens), and vice versa. The size of each data point is scaled by the number of classifications made by the user. The vast majority of users correctly identified most of the lenses and non-lenses that they were shown.

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