Disentangling the Galactic binary zoo: Machine learning classification of stellar remnant binaries in LISA data

TL;DR

This paper explores machine learning techniques, especially gradient boosting, to classify different types of stellar remnant binaries in simulated LISA gravitational-wave data, improving identification accuracy of various populations.

Contribution

It introduces a machine learning framework using ensemble methods to classify stellar remnant binaries in LISA data, addressing challenges of overlapping features and class imbalance.

Findings

Gradient boosting achieves 99% recall for white dwarf binaries.

High recall (≥85%) for high-mass binaries, including neutron star-white dwarf systems.

Machine learning outperforms traditional statistical methods in classifying rare binary types.

Abstract

The Laser Interferometer Space Antenna (LISA) will open a new observational window in the millihertz gravitational-wave band, enabling the detection of tens of thousands of compact stellar remnant binaries across the Milky Way. Most of LISA's sources will be double white dwarf (WDWD) systems, while neutron star-white dwarf (NSWD) binaries and higher-mass systems will be orders of magnitude rarer but of significant astrophysical interest. Disentangling these populations is challenging due to the strong overlap in their gravitational-wave features. In this work, we investigate the use of machine-learning techniques to classify LISA-detectable binaries based solely on LISA observables. Using mock catalogues of Galactic binaries constructed from population-synthesis studies, we evaluate a range of machine-learning classifiers. We find that ensemble-based methods-particularly gradient-boosting algorithms such as XGBoost-deliver the best performance on our highly imbalanced dataset. WDWD systems are identified with a recall of $\sim 99\%$, reflecting their dominant presence, and high-mass binaries are also classified with high recall ($\ge 85\%$). In contrast, NSWD systems remain the most challenging population to distinguish: their features overlap strongly with those of WDWD binaries, making them particularly prone to misclassification. Despite this, XGBoost correctly identifies 85.6% of NSWD systems in our simulated LISA detections, outperforming simple statistical approaches based on kernel density estimation. We further demonstrate that machine-learning classification can effectively support the interpretation of LISA data, enabling the identification of eccentric binaries and extremely rare subclasses.