摘要：This study involves two photometric catalogues, AllWISE and Pan-STARRS Data Release 1, which were cross-matched to identify extragalactic objects among the common sources of these catalogues. To separate galaxies and quasars from stars, we created a machine-learning model that is trained on photometric (in fact, colour-based) information from the optical and infrared wavelength ranges. The model is based on three important procedures: the construction of the autoencoder artificial neural network, separation of galaxies and quasars from stars with a support vector machine (SVM) classifier, and cleaning of the AllWISE × PS1 sample to remove sources with abnormal colour indices using a one-class SVM. As a training sample, we employed a set of spectroscopically confirmed sources from the Sloan Digital Sky Survey Data Release 14. Having applied the classification model to the data of crossing the AllWISE and Pan-STARRS DR1 samples, we created the Northern Extragalactic WISE × Pan-STARRS (NEWS) catalogue, containing 40 million extragalactic objects and covering 3/4 of celestial sphere up tog = 23m. Several independent classification quality tests, namely, the astrometric test along with others based on the use of data from spectroscopic surveys show similar results and indicate a high purity (∼98.0%) and completeness (> 98%) for the NEWS catalogue within the magnitude range of 19.0m < g < 22.5m. The classification quality still retains quite acceptable levels of 70% for purity and 97% for completeness for the brightest and faintest objects from this magnitude range. In addition, validation with external data sets has demonstrated the need for using only those sources in the NEWS catalogue that are outside the zone with the enhanced extinction. We show that the number of quasars from the NEWS catalogue identified inGaiaDR2 exceeds the number of quasars previously identified inGaiaDR2 with the use of the AllWISEAGN catalogue. These quasars may be used in future as an additional sample for testing and anchoring theGaiaCelestial Reference Frame.
关键词：enmethods: data analysiscatalogsgalaxies: statisticsreference systems
