ner_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation O B-Citation O B-Citation I-Citation I-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O B-Telescope O O O O O O O B-Survey B-Wavelength O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset I-Dataset B-Dataset O O B-Observatory O O O O O B-Model I-Model I-Model O O O O O O O B-Model I-Model I-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Formula I-Formula I-Formula I-Formula O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O O O O O O O O O O O O B-CelestialRegion I-CelestialRegion O O O O O B-Wavelength O O B-Survey O O O O O O O B-Survey O B-Survey O O O O O O O B-Wavelength O B-Wavelength O O O O O O O O O O O O O B-Wavelength O O O O O B-Telescope O O O O O O O B-Dataset O O O B-Telescope O B-Mission O O B-Citation I-Citation O B-Citation I-Citation I-Citation I-Citation O O O O O O B-Survey O B-Dataset O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey B-Citation I-Citation I-Citation I-Citation I-Citation O B-Survey B-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Dataset O O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O O O O O O O O O O B-Dataset I-Dataset O O O O O O B-Formula I-Formula I-Formula I-Formula O
tokens: a clear separation can be made between most stars and extragalactic objects, the separation between galaxies and quasars is less abrupt, reflecting the range of central-engine-dominated and host-dominated emission in these objects. As simple colour-magnitude diagnostics show, our classification generalises simple colour-magnitude cuts that have been proposed in the literature ( Stern et al. 2005 , 2012 ; Assef et al. 2013 ; Chehade et al. 2018 ), but it also deals with the overlap of different classes, especially towards the faint end in WISE magnitudes and towards higher-redshift quasars. The three VEXAS optical+IR classified tables, with object IDs, coordinates, optical and infrared magnitudes, including the imputed ones (which are flagged), and the probability to belong to each class, are publicly released and available for the scientific community as part of the VEXAS Data Collection (DR2) via the ESO Phase 3 13 . The machine-learning classification pipeline and the code are available on the VEXAS Github repository 14 . The density of extragalactic objects varies with the survey depth, quite expectedly. Considering only high-confidence objects for which the probability of belonging to the class is p class ≥ 0.7, our classifiers yielded 111 qso deg −2 for the VEXAS-DESW footprint (≈4900 deg 2 ) and 103 qso deg −2 for the VEXAS-PSW footprint (≈3800 deg 2 ). These numbers roughly meet the requirements of the 4MOST selection for baryon acoustic oscillations measurements. The density drops to ≈10 qso deg −2 for the VEXAS-SMW footprint (≈9300 deg 2 ). All in all, the combined survey depth is the limiting factor. The VEXAS-SMW footprint is limited by the shallow optical coverage of SkyMapper, which also has less uniform coverage than DES and PanSTARRS. A solution would be to consider only NIR and mid-IR magnitudes in the classification, which in turn requires more uniform coverage in the NIR and smaller uncertainties in the WISE magnitudes. This may be obtained thanks to unWISE, a re-processing of WISE, and NeoWISE imaging ( Lang 2014 ; Schlafly et al. 2019 ), or with forced photometry (on SkyMapper and unWISE cutouts) based on VISTA detections. We caution that these samples are only the very first step for cosmological measurements, which also require spectroscopic redshifts and well characterised coverage maps. The spectroscopic follow-up is already the aim of southern surveys (4MOST, de Jong et al. 2012 ; SDSS-V, Kollmeier et al. 2017 ). Since this paper is focused on the classification techniques and their application to VEXAS, the coverage maps are outside the scope of this work and will be part of a subsequent release. Appendix A: Testing the classification at the faint end of VEXAS-DESW As described in detail in Sect. 6.1 , the r -band magnitude ranges covered by the input tables are larger than those covered by the corresponding tables. This is particularly true for the VEXAS-DESW table, which reaches magnitudes as faint as r ∼ 25 m (see