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An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks

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An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks. / Dark Energy Survey Collaboration ; Collett, T.; Avila, S.

In: The Astrophysical Journal Supplement Series, Vol. 243, No. 1, 17, 19.07.2019.

Research output: Contribution to journalArticlepeer-review

Harvard

Dark Energy Survey Collaboration, Collett, T & Avila, S 2019, 'An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks', The Astrophysical Journal Supplement Series, vol. 243, no. 1, 17. https://doi.org/10.3847/1538-4365/ab26b6

APA

Dark Energy Survey Collaboration, Collett, T., & Avila, S. (2019). An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks. The Astrophysical Journal Supplement Series, 243(1), [17]. https://doi.org/10.3847/1538-4365/ab26b6

Vancouver

Dark Energy Survey Collaboration, Collett T, Avila S. An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks. The Astrophysical Journal Supplement Series. 2019 Jul 19;243(1). 17. https://doi.org/10.3847/1538-4365/ab26b6

Author

Dark Energy Survey Collaboration ; Collett, T. ; Avila, S. / An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks. In: The Astrophysical Journal Supplement Series. 2019 ; Vol. 243, No. 1.

Bibtex

@article{4ae51edd166340918985e5c809484401,
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 the Dark Energy Survey 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 = "astro-ph.GA, RCUK, STFC",
author = "{Dark Energy Survey Collaboration} and 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. Garcia-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",
year = "2019",
month = jul,
day = "19",
doi = "10.3847/1538-4365/ab26b6",
language = "English",
volume = "243",
journal = "The Astrophysical Journal Supplement Series",
issn = "0067-0049",
publisher = "IOP Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks

AU - Dark Energy Survey Collaboration

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 - Garcia-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.

PY - 2019/7/19

Y1 - 2019/7/19

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 the Dark Energy Survey 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 the Dark Energy Survey 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 - astro-ph.GA

KW - RCUK

KW - STFC

U2 - 10.3847/1538-4365/ab26b6

DO - 10.3847/1538-4365/ab26b6

M3 - Article

VL - 243

JO - The Astrophysical Journal Supplement Series

JF - The Astrophysical Journal Supplement Series

SN - 0067-0049

IS - 1

M1 - 17

ER -

ID: 14635227