2025/06/24 by Amoy Ashesh, Harsimran Kaur, Ashesh, Amoy +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Machine Learning in Bioinformatics
paper · doi:10.33232/001c.146027
openalex created_date 2025/10/17 · openalex publication_date 2025/10/17 · openalex updated_date 2026/08/03
We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ clustering and nearest neighbour search to pair candidate systems. Our approach incorporates data preprocessing techniques such as SMOTE, correlation analysis, and PCA, and achieves high accuracy and recall in the task of wide binary classification. The resulting publicly available code enables rapid, scalable, and customizable analysis of wide binaries, complementing conventional analyses and providing a valuable resource for future astrophysical studies.