Description
Within a Kilo-Degree Survey (KiDS) Strongly lensed QUAsar Detection project (KiDS-SQuaD), we built a catalogue of bright extragalactic objects from the KiDS DR4, with the main objective to select the reliable gravitationally lensed quasar candidates. We used machine learning algorithm, trained on Sloan Digital Sky Survey DR14 data, to classify sources from subsample (r<22mag) of KiDS DR4 on three classes: stars, quasars and galaxies. Resulting KiDS Bright EXtraGalactic Objects catalogue (KiDS-BEXGO) contains ~6M galaxies and ~0.2M quasars. KiDS-BEXGO represents the first comprehensive identification of bright extragalactic objects in the KiDS DR4 data.
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