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

TL;DR

This paper develops a random forest classifier to identify imaging artifacts in VLASS DRAGNs, significantly improving catalog purity and completeness.

Contribution

The authors train and optimize random forest models to classify artifacts in VLASS DRAGNs, achieving high accuracy and enabling a more reliable catalog of sources.

Findings

Achieved a weighted F1 score of 97.01% in artifact classification.

Produced a catalog with 99.3% completeness and 97.7% artifact-free sources.

Mitigated class imbalance through training set optimization.

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 $97.01\%^{+1.12\%}_{-1.32\%}$. 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.