Detecting wide binaries using machine learning algorithms

TL;DR

This paper introduces a machine learning framework that uses Gaia DR3 data to efficiently identify wide binary star systems, enhancing traditional methods.

Contribution

The authors develop a supervised ML approach with data preprocessing and clustering techniques, providing a scalable tool for wide binary detection.

Findings

High accuracy and recall in wide binary classification

Effective use of data preprocessing techniques like SMOTE and PCA

Publicly available code for scalable analysis

Abstract

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.