Parameter Estimation for Gravitational-wave Bursts with the BayesWave Pipeline

Area of Science:

• Astrophysics
• Gravitational-wave astronomy
• Data analysis

Background:

• The LIGO-Virgo Collaboration utilizes pipelines for gravitational-wave burst parameter estimation.
• Accurate characterization of gravitational-wave signals is crucial for understanding astrophysical sources.

Purpose of the Study:

• To comprehensively evaluate the performance of the BayesWave pipeline for gravitational-wave burst parameter estimation.
• To assess the pipeline's accuracy in sky localization, waveform reconstruction, and parameter estimation for various signal morphologies.

Main Methods:

• Simulated gravitational-wave signals (sine-Gaussians, Gaussians, white-noise bursts, binary black hole signals) were injected into simulated Advanced LIGO noise.
• The BayesWave pipeline was used to recover these signals, analyzing its performance across different metrics.

Main Results:

• BayesWave achieved comparable sky localization accuracy (median separation 25°.1–30°.3) for all tested morphologies.
• Waveform reconstruction accuracy improved with increasing network signal-to-noise ratio (S/N_net), reaching 85% match below S/N_net ≈ 20 and 95% below S/N_net ≈ 50.
• Parameter estimation accuracy was primarily limited by statistical errors in the frequency domain and systematic errors in the time domain for low-amplitude signal components.

Conclusions:

• The BayesWave pipeline demonstrates robust performance in localizing and reconstructing gravitational-wave bursts.
• Its accuracy is dependent on signal morphology and network signal-to-noise ratio, with clear improvements at higher signal strengths.
• The study introduces figures of merit for future pipeline characterizations and highlights limitations in reconstructing low-amplitude signal features.