LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks
Date Issued
2019
Author(s)
Petrillo, C. E.
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Vernardos, G.
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Koopmans, L. V. E.
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Verdoes Kleijn, G.
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Bilicki, M.
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Chatterjee, S.
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Covone, G.
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Dvornik, A.
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Erben, T.
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Giblin, B.
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Heymans, C.
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de Jong, J. T. A.
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Kuijken, K.
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Schneider, P.
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Shan, H.
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•
Wright, A. H.
Abstract
We present a new sample of galaxy-scale strong gravitational lens candidates, selected from 904 deg2 of Data Release 4 of the Kilo-Degree Survey, i.e. the `Lenses in the Kilo-Degree Survey' (LinKS) sample. We apply two convolutional neural networks (ConvNets) to {∼ }88 000 colour-magnitude-selected luminous red galaxies yielding a list of 3500 strong lens candidates. This list is further downselected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as `potential lens candidates' by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or Large Synoptic Survey Telescope data can select a sample of ∼3000 lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.
Volume
484
Issue
3
Start page
3879
Issn Identifier
0035-8711
Ads BibCode
2019MNRAS.484.3879P
Rights
open.access
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