2021/03/16 by V. Khramtsov, Vladislav Khramtsov, Chiara Spiniello +5
Engineering · Physics and Astronomy · #Astronomical Observations and Instrumentation #Astronomy #Astrophysics #Extension (predicate logic) #Physics #Programming language #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.1051/0004-6361/202040131
published as A&A 651, A69 (2021) · 25 pages, 18 figures, 8 tables. Accepted for publication on A&A. The VEXAS tables are publicly available through the ESO Phase 3 here: https://archive.eso.org/scienceportal/home?data_collection=VEXAS. The DR2 tables update the DR1 with the addition of imputed magnitudes and membership probabilities to each of the three classes
arxiv created 2021/03/16 · openalex created_date 2021/03/29 · openalex publication_date 2021/05/17 · arxiv updated 2021/07/21 · openalex updated_date 2026/08/06
Context. We present the second public data release of the VISTA EXtension to Auxiliary Surveys (VEXAS), where we classify objects into stars, galaxies, and quasars based on an ensemble of machine learning algorithms. Aims. The aim of VEXAS is to build the widest multi-wavelength catalogue, providing reference magnitudes, colours, and morphological information for a large number of scientific uses. Methods. We applied an ensemble of thirty-two different machine learning models, based on three different algorithms and on different magnitude sets, training samples, and classification problems (two or three classes) on the three VEXAS Data Release 1 (DR1) optical and infrared (IR) tables. The tables were created in DR1 cross-matching VISTA near-infrared data with Wide-field Infrared Survey Explorer far-infrared data and with optical magnitudes from the Dark Energy Survey (VEXAS-DESW), the Sky Mapper Survey (VEXAS-SMW), and the Panoramic Survey Telescope and Rapid Response System Survey (VEXAS-PSW). We assembled a large table of spectroscopically confirmed objects (VEXAS-SPEC-GOOD, 415 628 unique objects), based on the combination of six different spectroscopic surveys that we used for training. We developed feature imputation to also classify objects for which magnitudes in one or more bands are missing. Results. We classify in total ≈90 × 10 6 objects in the Southern Hemisphere. Among these, ≈62.9 × 10 6 (≈52.6 × 10 6 ) are classified as ‘high confidence’ (‘secure’) stars, ≈920 000 (≈750 000) as ‘high confidence’ (‘secure’) quasars, and ≈34.8 (≈34.1) million as ‘high confidence’ (‘secure’) galaxies, with p class ≥ 0.7 ( p class ≥ 0.9). The DR2 tables update the DR1 with the addition of imputed magnitudes and membership probabilities to each of the three classes. Conclusions. The density of high-confidence extragalactic objects varies strongly with the survey depth: at p class > 0.7, there are 11 deg −2 quasars in the VEXAS-DESW footprint and 103 deg −2 in the VEXAS-PSW footprint, while only 10.7 deg −2 in the VEXAS-SM footprint. Improved depth in the mid-infrared and coverage in the optical and near-infrared are needed for the SM footprint that is not already covered by DESW and PSW.