# Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression

## TL;DR

This paper develops a fast, accurate frequency-domain surrogate model for aligned-spin binary neutron star waveforms using Gaussian process regression, enabling efficient parameter estimation for gravitational wave data.

## Contribution

It introduces a Gaussian process regression-based surrogate for the SEOBNRv4T waveform model, significantly reducing evaluation time while maintaining high fidelity.

## Key findings

- Maximum mismatch of 4.5e-4 for LIGO
- Evaluation time reduced to 0.13 seconds
- Accurate parameter recovery in tests

## Abstract

Fast and accurate waveform models are necessary for measuring the properties of inspiraling binary neutron star systems such as GW170817. We present a frequency-domain surrogate version of the aligned-spin binary neutron star waveform model using the effective one body formalism known as SEOBNRv4T. This model includes the quadrupolar and octopolar adiabatic and dynamical tides. The version presented here is improved by the inclusion of the spin-induced quadrupole moment effect, and completed by a prescription for tapering the end of the waveform to qualitatively reproduce numerical relativity simulations. The resulting model has 14 intrinsic parameters. We reduce its dimensionality by using universal relations that approximate all matter effects in terms of the leading quadrupolar tidal parameters. The implementation of the time-domain model can take up to an hour to evaluate using a starting frequency of 20Hz, and this is too slow for many parameter estimation codes that require $O(10^7)$ sequential waveform evaluations. We therefore construct a fast and faithful frequency-domain surrogate of this model using Gaussian process regression. The resulting surrogate has a maximum mismatch of $4.5\times 10^{-4}$ for the Advanced LIGO detector, and requires 0.13s to evaluate for a waveform with a starting frequency of 20Hz. Finally, we perform an end-to-end test of the surrogate with a set of parameter estimation runs, and find that the surrogate accurately recovers the parameters of injected waveforms.