LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networksPetrillo, E., Tortora, C., Vernardos, G., Koopmans, L. V. E., Verdoes Kleijn, G., Bilicki, M., Napolitano, N., Chatterjee, S., Covone, G., Dvornik, A., Erben, T., Getman, F., Giblin, B., Heymans, C., de Jong, J. T. A., Kuijken, K., Schneider, P., Huang, S., Spiniello, C. & Wright, A. H., Apr-2019, In : Monthly Notices of the Royal Astronomical Society. 484, 3, p. 3879–3896
Research output: Contribution to journal › Article › Academic › peer-review
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 ∼88000 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.
|Journal||Monthly Notices of the Royal Astronomical Society|
|Publication status||Published - Apr-2019|
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