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. Burke; A. 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

doi:10.3847/1538-4365/ab26b6

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. Burke

A. 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 journal › Article › peer-review

130 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 language	English (US)
Article number	17
Journal	Astrophysical Journal, Supplement Series
Volume	243
Issue number	1
DOIs	https://doi.org/10.3847/1538-4365/ab26b6
State	Published - 2019

Keywords

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

ASJC Scopus subject areas

Astronomy and Astrophysics
Space and Planetary Science

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10.3847/1538-4365/ab26b6

Cite this

Jacobs, C., Collett, T., Glazebrook, K., Buckley-Geer, E., Diehl, H. T., Lin, H., McCarthy, C., Qin, A. K., Odden, C., Escudero, M. C., Dial, P., Yung, V. J., Gaitsch, S., Pellico, A., Lindgren, K. A., Abbott, T. M. C., Annis, J., Avila, S., Brooks, D., ... Zhang, Y. (2019). An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in des Using Convolutional Neural Networks. Astrophysical Journal, Supplement Series, 243(1), Article 17. https://doi.org/10.3847/1538-4365/ab26b6

Jacobs, C, Collett, T, Glazebrook, K, Buckley-Geer, E, Diehl, HT, Lin, H, McCarthy, C, Qin, AK, Odden, C, Escudero, MC, Dial, P, Yung, VJ, Gaitsch, S, Pellico, A, Lindgren, KA, Abbott, TMC, Annis, J, Avila, S, Brooks, D, Burke, DL, Rosell, AC, Kind, MC, Carretero, J, Costa, LND, Vicente, JD, Fosalba, P, Frieman, J, García-Bellido, J, Gaztanaga, E, Goldstein, DA, Gruen, D, Gruendl, RA, Gschwend, J, Hollowood, DL, Honscheid, K, Hoyle, B, James, DJ, Krause, E, Kuropatkin, N, Lahav, O, Lima, M, Maia, MAG, Marshall, JL, Miquel, R, Plazas, AA, Roodman, A, Sanchez, E, Scarpine, V, Serrano, S, Sevilla-Noarbe, I, Smith, M, Sobreira, F, Suchyta, E, Swanson, MEC, Tarle, G, Vikram, V, Walker, AR & Zhang, Y 2019, 'An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in des Using Convolutional Neural Networks', Astrophysical Journal, Supplement Series, vol. 243, no. 1, 17. https://doi.org/10.3847/1538-4365/ab26b6

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

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.",

keywords = "gravitational lensing: strong, methods: data analysis, methods: statistical, surveys",

author = "C. Jacobs and T. Collett and K. Glazebrook and E. Buckley-Geer and Diehl, \{H. T.\} and H. Lin and C. McCarthy and Qin, \{A. K.\} and C. Odden and Escudero, \{M. Caso\} and P. Dial and Yung, \{V. J.\} and S. Gaitsch and A. Pellico and Lindgren, \{K. A.\} and Abbott, \{T. M.C.\} and J. Annis and S. Avila and D. Brooks and Burke, \{D. L.\} and Rosell, \{A. Carnero\} and Kind, \{M. Carrasco\} and J. Carretero and Costa, \{L. N.Da\} and Vicente, \{J. De\} and P. Fosalba and J. Frieman and J. Garc{\'i}a-Bellido and E. Gaztanaga and Goldstein, \{D. A.\} and D. Gruen and Gruendl, \{R. A.\} and J. Gschwend and Hollowood, \{D. L.\} and K. Honscheid and B. Hoyle and James, \{D. J.\} and E. Krause and N. Kuropatkin and O. Lahav and M. Lima and Maia, \{M. A.G.\} and Marshall, \{J. L.\} and R. Miquel and Plazas, \{A. A.\} and A. Roodman and E. Sanchez and V. Scarpine and S. Serrano and I. Sevilla-Noarbe and M. Smith and F. Sobreira and E. Suchyta and Swanson, \{M. E.C.\} and G. Tarle and V. Vikram and Walker, \{A. R.\} and Y. Zhang",

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AU - Jacobs, C.

AU - Collett, T.

AU - Glazebrook, K.

AU - Buckley-Geer, E.

AU - Diehl, H. T.

AU - Lin, H.

AU - McCarthy, C.

AU - Qin, A. K.

AU - Odden, C.

AU - Escudero, M. Caso

AU - Dial, P.

AU - Yung, V. J.

AU - Gaitsch, S.

AU - Pellico, A.

AU - Lindgren, K. A.

AU - Abbott, T. M.C.

AU - Annis, J.

AU - Avila, S.

AU - Brooks, D.

AU - Burke, D. L.

AU - Rosell, A. Carnero

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Costa, L. N.Da

AU - Vicente, J. De

AU - Fosalba, P.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gaztanaga, E.

AU - Goldstein, D. A.

AU - Gruen, D.

AU - Gruendl, R. A.

AU - Gschwend, J.

AU - Hollowood, D. L.

AU - Honscheid, K.

AU - Hoyle, B.

AU - James, D. J.

AU - Krause, E.

AU - Kuropatkin, N.

AU - Lahav, O.

AU - Lima, M.

AU - Maia, M. A.G.

AU - Marshall, J. L.

AU - Miquel, R.

AU - Plazas, A. A.

AU - Roodman, A.

AU - Sanchez, E.

AU - Scarpine, V.

AU - Serrano, S.

AU - Sevilla-Noarbe, I.

AU - Smith, M.

AU - Sobreira, F.

AU - Suchyta, E.

AU - Swanson, M. E.C.

AU - Tarle, G.

AU - Vikram, V.

AU - Walker, A. R.

AU - Zhang, Y.

PY - 2019

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N2 - 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.

AB - 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.

KW - gravitational lensing: strong

KW - methods: data analysis

KW - methods: statistical

KW - surveys

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DO - 10.3847/1538-4365/ab26b6

M3 - Article

AN - SCOPUS:85071163691

SN - 0067-0049

VL - 243

JO - Astrophysical Journal, Supplement Series

JF - Astrophysical Journal, Supplement Series

IS - 1

M1 - 17

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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Strong DES lens candidates from neural networks

Cite this

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Datasets

Strong DES lens candidates from neural networks

Cite this