Using machine learning to parametrize postmerger signals from binary neutron stars

TL;DR

This paper introduces a machine learning approach using a conditional variational autoencoder to model postmerger gravitational wave signals from binary neutron stars, addressing uncertainties and limited data in waveform construction.

Contribution

It demonstrates that a CVAE can accurately generate postmerger waveforms and encode physical parameters like the equation of state within its latent space.

Findings

CVAE accurately reproduces synthetic postmerger waveforms

The model encodes the neutron star equation of state in latent variables

Proof-of-principle shows potential for future waveform modeling

Abstract

There is growing interest in the detection and characterization of gravitational waves from postmerger oscillations of binary neutron stars. These signals contain information about the nature of the remnant and the high-density and out-of-equilibrium physics of the postmerger processes, which would complement any electromagnetic signal. However, the construction of binary neutron star postmerger waveforms is much more complicated than for binary black holes: (i) there are theoretical uncertainties in the neutron-star equation of state and other aspects of the high-density physics, (ii) numerical simulations are expensive and available ones only cover a small fraction of the parameter space with limited numerical accuracy, and (iii) it is unclear how to parametrize the theoretical uncertainties and interpolate across parameter space. In this work, we describe the use of a machine-learning method called a conditional variational autoencoder (CVAE) to construct postmerger models for hyper/massive neutron star remnant signals based on numerical-relativity simulations. The CVAE provides a probabilistic model, which encodes uncertainties in the training data within a set of latent parameters. We estimate that training such a model will ultimately require $\sim 10^4$ waveforms. However, using synthetic training waveforms as a proof-of-principle, we show that the CVAE can be used as an accurate generative model and that it encodes the equation of state in a useful latent representation.