Table · dataset · 2024
J-PLUS Bayesian object classification
Listed in IVOA Registry (Virtual Observatory)
With its 12 optical filters, the Javalambre-Photometric Local Universe Survey (J-PLUS) provides an unprecedented multicolor view of the local Universe.
Description
The third data release (DR3) covers 3,192 deg^2 and contains 47.4 million objects. However, the classification algorithms currently implemented in the J-PLUS pipeline are deterministic and based solely on the sources morphology of the sources.
Our goal is to classify the sources identified in the J-PLUS DR3 images into stars, quasi-stellar objects (QSOs), and galaxies. For this task, we present BANNJOS, a machine learning pipeline that utilizes Bayesian neural networks to provide the full probability distribution function (PDF) of the classification. BANNJOS is trained on photometric, astrometric, and morphological data from J-PLUS DR3, Gaia DR3, and Catwise, using over 1.2 million objects with spectroscopic classification from SDSS DR18, LAMOST DR9, DESI Early Data Release, and Gaia DR3.
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Results are validated on a test set of about 140,000 objects and cross-checked against theoretical model predictions. BANNJOS outperforms all previous classifiers in terms of accuracy, precision, and completeness across the entire magnitude range. It delivers over 95% accuracy for objects brighter than r=21.5 mag, and ~90% accuracy for those up to r=22mag, where J-PLUS completeness is <25%.
BANNJOS is also the first object classifier to provide the full probability distribution function (PDF) of the classification, enabling precise object selection for high purity or completeness, and for identifying objects with complex features, like active galactic nuclei with resolved host galaxies. BANNJOS has effectively classified J-PLUS sources into around 20 million galaxies, 1 million QSOs, and 26 million stars, with full PDFs for each, which allow for later refinement of the sample.
The upcoming J-PAS survey, with its 56 color bands, will further enhance BANNJOS's ability to detail the nature of each source.
Links
Where it is published
- Reference page cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/691/A221 ↗
landing page · from IVOA Registry
Documentation and papers
- ADS 2024A&A...691A.221D ui.adsabs.harvard.edu/abs/2024A%26A...691A.221D ↗
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/691/a221 ↗
catalogue entry · from IVOA Registry
Topics
- From keywords
- Astronomy & Astrophysics
- Inferred from text
- Image 65%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| IVOA Registry (Virtual Observatory) | ivo://cds.vizier/j/a+a/691/a221 | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[field].local:field:astronomy | mapping · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · IVOA Registry | keyword-concept-rules@1.0.0 | title+description (65%) |
| 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 |