Table · dataset · 2019
KiDS DR3 QSO catalog
Listed in IVOA Registry (Virtual Observatory)
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3).
Description
The QSOs are identified by the random forest (RF) supervised machine learning model, trained on Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of entries with excessively noisy, missing or otherwise problematic measurements.
Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS.
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This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97% (percentage of correctly classified objects), purity of 91% (percentage of true quasars within the objects classified as such), and completeness of 87% (detection ratio of all true quasars), as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint.
The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from the Wide-field Infrared Survey Explorer (WISE). An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of pQSO>0.8 is optimal for purity, whereas pQSO>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.
We publicly release the resulting catalog at kids.strw.leidenuniv.nl/DR3/quasarcatalog.php, and the code at github.com/snakoneczny/kids-quasars
Links
Where it is published
- Reference page cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/624/A13 ↗
landing page · from IVOA Registry
Documentation and papers
- ADS 2019A&A...624A..13N ui.adsabs.harvard.edu/abs/2019A%26A...624A..13N ↗
publication · from IVOA Registry
Catalogue records · 2
- VOResource record dc.g-vo.org/oai.xml?verb=GetRecord&metadataPrefix=ivo_vor&identifier=ivo%3… ↗
metadata API · from IVOA Registry
- Registry record (GAVO) dc.g-vo.org/I/cds.vizier/j/a%2Ba/624/a13 ↗
catalogue entry · from IVOA Registry
Topics
- From keywords
- Astronomy & Astrophysics
- Inferred from text
- Astronomical sciences 73%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| IVOA Registry (Virtual Observatory) | ivo://cds.vizier/j/a+a/624/a13 | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[field].anzsrc:group:5101 | enrichment · IVOA Registry | taxonomy-embedding@1.1.0 | title+keywords+description (73%) |
| concepts[field].local:field:astronomy | mapping · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| description | source · IVOA Registry | connector:ivoa_registry@1.0.0 | rr.resource.res_description |
| license_text | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| publication_date | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| title | source · IVOA Registry | connector:ivoa_registry@1.0.0 | rr.resource.res_title |
| updated_date | source · IVOA Registry | connector:ivoa_registry@1.0.0 |