English

Multi-detector characterization of gravitational-wave burst tensor polarizations with the BayesWave algorithm

General Relativity and Quantum Cosmology 2025-04-03 v1 Instrumentation and Methods for Astrophysics

Abstract

Einstein's theory of general relativity predicts that gravitational waves (GWs) are tensor-polarized, with two modes of polarization: plus (h+h_+) and cross (h×h_\times). The unmodeled GW burst analysis pipeline, \textit{BayesWave}, offers two tensor-polarized signal models: the elliptical polarization model (EE) and the relaxed polarization model (RR). Future expansion of the global GW detector network will enable more accurate studies of GW polarizations with GW bursts. Here a multi-detector analysis is conducted to compare the performance of EE and RR in characterizing elliptical and nonelliptical GW polarizations, using nonprecessing and precessing binary black holes (BBHs) respectively as representative synthetic sources. It is found that both models reconstruct the elliptical nonprecessing BBH signals accurately, but EE has a higher Bayesian evidence than RR as it is has fewer model parameters. The same is true for precessing BBHs that are reconstructed equally well by both models. However, for some events with high precession and especially with three or more detectors, the reconstruction accuracy and evidence of RR surpass EE. The analysis is repeated for BBH events from the third LIGO-Virgo-KAGRA observing run, and the results show that EE is preferred over RR for existing detections. Although EE is generally preferred for its simplicity, it insists on elliptical polarizations, whereas RR can measure generic GW polarization content in terms of Stokes parameters. The accuracy of RR in recovering polarization content improves as the detector network expands, and the performance is independent of the GW signal morphology.

Keywords

Cite

@article{arxiv.2503.08050,
  title  = {Multi-detector characterization of gravitational-wave burst tensor polarizations with the BayesWave algorithm},
  author = {Yi Shuen C. Lee and Siddhant Doshi and Margaret Millhouse and Andrew Melatos},
  journal= {arXiv preprint arXiv:2503.08050},
  year   = {2025}
}

Comments

17 pages, 7 figures

R2 v1 2026-06-28T22:15:14.580Z