2018/12/31 by C. E. Petrillo, C. Tortora, G. Vernardos +29 · 3 citations
Physics and Astronomy · #Adaptive optics and wavefront sensing #Artificial intelligence #Astronomy #Astronomy and Astrophysical Research #Astrophysics #Computer science #Convolutional neural network #Degree (music) #Galaxies: Formation, Evolution, Phenomena #Galaxy #Physics #Scale (ratio) #astro-ph.GA
paper · pdf · doi:10.1093/mnras/stz189
19 pages, 11 figures, accepted for publication in MNRAS
arxiv created 2019/01/16 · openalex publication_date 2019/01/16 · arxiv updated 2019/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.