DRAGNs in the Forest: Identifying Artifacts with Random Forest Models in the VLASS DRAGNs Catalog

Abstract

The Quick Look data products from the Very Large Array Sky Survey (VLASS) contain widespread imaging artifacts arising from the simplified imaging algorithm used in their production. The catalog of double radio sources associated with active galactic nuclei (DRAGNs) found in the VLASS first epoch Quick Look release using the DRAGNhunter algorithm suffers from contamination from these artifacts. These sources contain two or three individual components, each of which can be an artifact. We train random forest models to classify these DRAGNs based on the number of artifacts they contain, ranging from zero to three artifacts. We optimize our models and mitigate the class imbalance of our dataset with judicious training set selection, and the best of our models achieves a weighted F1 score of $97.01{\%}_{-1.32\%}^{+1.12\%}$. Using our classifications, we produce a catalog of VLASS DRAGNs from which an estimated 99.3% complete catalog of 97.7% artifact-free sources can be extracted.

23 pages, 14 figures

References in corpus (24)

• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Hyperparameters and Tuning Strategies for Random Forest
• Preliminary Results from NEOWISE: An Enhancement to the Wide-field Infrared Survey Explorer for Solar System Science
• The ASAS-SN Catalog of Variable Stars I: The Serendipitous Survey
• unWISE: unblurred coadds of the WISE imaging
• The Last of FIRST: The Final Catalog and Source Identifications
• Australian Square Kilometre Array Pathfinder: I. System Description
• Radio galaxies and feedback from AGN jets
• TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests
• A Quick Look at the $3$GHz Radio Sky I. Source Statistics from the Very Large Array Sky Survey
• Full-depth Coadds of the WISE and First-year NEOWISE-Reactivation Images
• Deep Full-sky Coadds from Three Years of WISE and NEOWISE Observations
• Stellar Spectra Classification and Feature evaluation Based on Random Forest
• A Quick Look at the 3GHz Radio Sky. II. Hunting for DRAGNs in the VLA Sky Survey
• The Evolutionary Map of the Universe: A new radio atlas for the southern hemisphere sky
• A Random Forest spectral classification of the Gaia 500-pc white dwarf population
• Selection of optically variable active galactic nuclei via a random forest algorithm
• Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images
• Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
• EMU and the DRAGNs I: A Catalogue of DRAGNs