An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in des Using Convolutional Neural Networks

C. Jacobs, T. Collett, K. Glazebrook, E. Buckley-Geer, H. T. Diehl, H. Lin, C. McCarthy, A. K. Qin, C. Odden, M. Caso Escudero, P. Dial, V. J. Yung, S. Gaitsch, A. Pellico, K. A. Lindgren, T. M.C. Abbott, J. Annis, S. Avila, D. Brooks, D. L. BurkeA. Carnero Rosell, M. Carrasco Kind, J. Carretero, L. N.Da Costa, J. De Vicente, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. A. Goldstein, D. Gruen, R. A. Gruendl, J. Gschwend, D. L. Hollowood, K. Honscheid, B. Hoyle, D. J. James, E. Krause, N. Kuropatkin, O. Lahav, M. Lima, M. A.G. Maia, J. L. Marshall, R. Miquel, A. A. Plazas, A. Roodman, E. Sanchez, V. Scarpine, S. Serrano, I. Sevilla-Noarbe, M. Smith, F. Sobreira, E. Suchyta, M. E.C. Swanson, G. Tarle, V. Vikram, A. R. Walker, Y. Zhang

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

Abstract

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 discovered through visual inspection of blue-near-red objects in the DES catalog, we present a catalog of 511 lens candidates.

Original languageEnglish
Article number17
JournalAstrophysical Journal, Supplement Series
Volume243
Issue number1
DOIs
StatePublished - 2019

Funding

FundersFunder number
National Science Foundation1138766
Seventh Framework Programme240672, 306478, 291329

    Keywords

    • gravitational lensing: strong
    • methods: data analysis
    • methods: statistical
    • surveys

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