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

Astronomy and Astrophysics, vol. 714, pp. A76

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 investigated 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 evaluated 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. Double white dwarf systems are identified with a recall of ∼99%, reflecting their dominant presence. High-mass binaries are also classified with high recall (≥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.

Authors 3

1. Irwin Khai Cheng Tay

   Max Planck Institute for Astrophysics

   Affiliation as printed

   Max-Planck-Institut für Astrophysik

2. Valeriya Korol Aachen Leiden Observatory SRON Space Research Organisation Netherlands

   Leiden University · Max Planck Institute for Astrophysics · Leiden Observatory · Space Research Organisation Netherlands

   Affiliation as printed

   Leiden Observatory, Leiden University

   Max-Planck-Institut für Astrophysik

   SRON Space Research Organisation Netherlands

3. Thibault Lechien

   Max Planck Institute for Astrophysics

   Affiliation as printed

   Max-Planck-Institut für Astrophysik