An extended catalog of galaxy–galaxy strong gravitational lenses discovered in DES using convolutional neural networks
- Jacobs, C.
- Collett, T.
- Glazebrook, K.
- Buckley-Geer, E.
- Diehl, H.
- Lin, H.
- McCarthy, C.
- Qin, A.
- Odden, C.
- Escudero, M.
- Dial, P.
- Yung, V.
- Gaitsch, S.
- Pellico, A.
- Lindgren, K.
- Abbott, T.
- Annis, J.
- Avila, S.
- Brooks, D.
- Burke, D.
- Rosell, A.
- Kind, M.
- Carretero, J.
- da Costa, L.
- Vicente, J.
- Fosalba, P.
- Frieman, J.
- García-Bellido, J.
- Gaztanaga, E.
- Goldstein, D.
- Gruen, D.
- Gruendl, R.
- Gschwend, J.
- Hollowood, D.
- Honscheid, K.
- Hoyle, B.
- James, D.
- Krause, E.
- Kuropatkin, N.
- Lahav, O.
- Lima, M.
- Maia, M.
- Marshall, J.
- Miquel, R.
- Plazas, A.
- Roodman, A.
- Sanchez, E.
- Scarpine, V.
- Serrano, S.
- Sevilla-Noarbe, I.
- Smith, M.
- Sobreira, F.
- Suchyta, E.
- Swanson, M.
- Tarle, G.
- Vikram, V.
- Walker, A.
- Zhang, Y.
DOIAbstract
We search Dark Energy Survey (DES) Year 3 imaging for galaxy–galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a wider range of redshifts and colors. We train two neural networks using images of simulated lenses, then use them to score postage-stamp images of 7.9 million sources from DES chosen to have plausible lens colors based on simulations. We examine 1175 of the highest-scored candidates and identify 152 probable or definite lenses. Examining an additional 20,000 images with lower scores, we identify a further 247 probable or definite candidates. After including 86 candidates discovered in earlier searches using neural networks and 26 candidates discover...
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