English

Optimizing searches for gravitational wave bursts using coherent WaveBurst 2G

General Relativity and Quantum Cosmology 2025-10-27 v1 Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability

Abstract

The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal waveform, direction, or arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available emission models and for discovering yet-to-be-uncovered sources. Here, we discuss progress in the detection performance of the coherent WaveBurst second-generation pipeline (cWB-2G), which is highly adaptable to both minimally modeled and model-informed searches for GWTs. Several search configurations for GWTs are examined using approximately 14.8 days of observation time from the third observing run by LIGO-Virgo-KAGRA (LVK). Recent enhancements include a ranking statistic fully based on multivariate classification with eXtreme Gradient Boosting, a thorough validation of the statistical significance accuracy of GWT candidates, and a measurement of the correlations of false alarms and simulated detections between different concurrent searches. For the first time, we provide a comprehensive comparison of cWB-2G performance on data from networks made of two and three detectors, and we demonstrate the advantage of combining concurrent searches for GWTs of generic morphology in a global observatory. This work offers essential insights for assessing our data analysis strategies in ongoing and future LVK searches for generic GWTs.

Keywords

Cite

@article{arxiv.2510.21411,
  title  = {Optimizing searches for gravitational wave bursts using coherent WaveBurst 2G},
  author = {Alessandro Martini and Andrea Miani and Marco Drago and Claudia Lazzaro and Francesco Salemi and Sophie Bini and Osvaldo Freitas and Edoardo Milotti and Giacomo Principe and Shubhanshu Tiwari and Agata Trovato and Gabriele Vedovato and Yumeng Xu and Giovanni Andrea Prodi},
  journal= {arXiv preprint arXiv:2510.21411},
  year   = {2025}
}
R2 v1 2026-07-01T07:03:51.935Z