Details

Autor(en) / Beteiligte

• Petrillo, C E
• Tortora, C
• Vernardos, G
• Koopmans, L V E
• Verdoes Kleijn, G
• Bilicki, M
• Napolitano, N R
• Chatterjee, S
• Covone, G
• Dvornik, A
• Erben, T
• Getman, F
• Giblin, B
• Heymans, C
• de Jong, J T A
• Kuijken, K
• Schneider, P
• Shan, H
• Spiniello, C
• Wright, A H

Titel

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2019-04, Vol.484 (3), p.3879-3896

Ort / Verlag

Oxford University Press

Erscheinungsjahr

2019

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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 ${\sim }88\,000$ 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.