# Item

ITEM ACTIONSEXPORT

Released

Journal Article

#### Using machine learning to parametrize postmerger signals from binary neutron stars

##### External Resource

No external resources are shared

##### Fulltext (restricted access)

There are currently no full texts shared for your IP range.

##### Fulltext (public)

2201.06461.pdf

(Preprint), 2MB

##### Supplementary Material (public)

There is no public supplementary material available

##### Citation

Whittaker, T., East, W. E., Green, S., Lehner, L., & Yang, H. (2022). Using machine
learning to parametrize postmerger signals from binary neutron stars.* Physical Review D,*
*105*(12): 124021. doi:10.1103/PhysRevD.105.124021.

Cite as: https://hdl.handle.net/21.11116/0000-000A-99C7-9

##### 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.

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.