Detection and parameter estimation of binary neutron star merger remnants

TL;DR

This paper introduces a fast, simple gravitational waveform model for binary neutron star merger remnants, enabling effective detection and precise parameter estimation of post-merger signals, which enhances understanding of dense matter physics.

Contribution

The authors develop a three-component exponentially-damped sinusoid model for post-merger gravitational waves, achieving high fitting accuracy and enabling improved detection and parameter estimation.

Findings

Median fitting factors > 0.90 with numerical simulations.

Remnants detectable at SNR ≥ 7 using Bayes-factor statistic.

Frequency and tidal parameter constraints improve with higher SNR.

Abstract

Detection and parameter estimation of binary neutron star merger remnants can shed light on the physics of hot matter at supranuclear densities. Here we develop a fast, simple model that can generate gravitational waveforms, and show it can be used for both detection and parameter estimation of post-merger remnants. The model consists of three exponentially-damped sinusoids with a linear frequency-drift term. The median fitting factors between the model waveforms and numerical-relativity simulations exceed 0.90. We detect remnants at a post-merger signal-to-noise ratio of $\ge 7$ using a Bayes-factor detection statistic with a threshold of 3000. We can constrain the primary post-merger frequency to $\pm_{1.2}^{1.4}\%$ at post-merger signal-to-noise ratios of 15 with an increase in precision to $\pm_{0.2}^{0.3}\%$ for post-merger signal-to-noise ratios of 50. The tidal coupling constant can be constrained to $\pm^{9}_{12}\%$ at post-merger signal-to-noise ratios of 15, and $\pm 5\%$ at post-merger signal-to-noise ratios of 50 using a hierarchical inference model.