Robust and fast parameter estimation for gravitational waves from binary neutron star merger remnants

TL;DR

This paper introduces a fast, robust method for estimating parameters of gravitational waves from neutron star mergers' post-merger phase, combining analytic models with empirical relations for efficient inference and validation.

Contribution

The authors develop a novel parameter estimation approach that integrates an analytic waveform model with empirical relations, enabling efficient and robust analysis of post-merger gravitational wave signals.

Findings

High fitting factors across various neutron star equations of state.

Significant computational speed-up using Preconditioned Monte Carlo sampling.

Method applicable to future third-generation gravitational wave detectors.

Abstract

We present a robust and efficient methodology for parameter estimation of gravitational waves generated during the post-merger phase of binary neutron star mergers. Our approach leverages an analytic waveform model combined with empirical relations to predict prior ranges for the post-merger frequencies based on measurements of the chirp mass and effective tidal deformability in the inspiral phase. This enables robust inference of the main features of the post-merger spectrum, avoiding possible multi-modality induced by wide priors. Using waveforms derived from numerical relativity, we systematically validate our model across a broad spectrum of neutron star equations of state and mass configurations, demonstrating high fitting factors. Our method can be applied in future detections of gravitational waves from the post-merger phase with third-generation gravitational wave observatories. Furthermore, by integrating the Preconditioned Monte Carlo sampling method within the pocoMC framework, we achieve substantial computational acceleration compared to conventional Bayesian techniques.