Astrometric Binary Classification Via Artificial Neural Networks

TL;DR

This paper presents a machine learning approach using artificial neural networks to efficiently classify astrometric binary stars in Gaia data, achieving high accuracy and speed compared to traditional methods.

Contribution

The study introduces a novel ANN-based classification method for astrometric binaries, trained on Gaia DR3 data, offering a faster and highly accurate alternative to existing techniques.

Findings

Achieved 99.3% classification accuracy

High precision and recall rates of 0.988 and 0.991

AUC of 0.999 indicating excellent model performance

Abstract

With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the current computational methods employed to inspect these astrometric binary candidates are both computationally expensive and cannot be executed in a reasonable time frame. In light of this, a machine learning (ML) technique to automatically classify whether a set of stars belong to an astrometric binary pair via an artificial neural network (ANN) is proposed. Using data from Gaia DR3, the ANN was trained and tested on 1.5 million highly probable true and visual binaries, considering the proper motions, parallaxes, and angular and physical separations as features. The ANN achieves high classification scores, with an accuracy of 99.3%, a precision rate of 0.988, a recall rate of 0.991, and an AUC of 0.999, indicating that the utilized ML technique is a highly effective method for classifying astrometric binaries. Thus, the proposed ANN is a promising alternative to the existing methods for the classification of astrometric binaries.