Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning

Abstract

We present mlgw-bns, a gravitational waveform surrogate that allows for a significant improvement in the generation speed of frequency-domain waveforms for binary neutron star mergers, at a negligible cost in accuracy. This improvement is achieved by training a machine-learning model on a dataset of waveforms generated with an accurate but comparatively costlier approximant: the state-of-the-art effective-one-body model TEOBResumSPA. When coupled to a reduced-order scheme, mlgw-bns can accelerate waveform generation up to a factor of ~35, outperforming all other approximants of similar accuracy. By analyzing GW170817 in realistic parameter estimation settings with our scheme, we showcase an overall speedup against TEOBResumSPA greater than an order of magnitude. Our methodology will bear a significant impact on the scientific program of next generation detectors by allowing routine usage of accurate effective-one-body models.

20 pages, 11 figures

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Cited by in corpus (16)

• SEOBNRv5PHM: Next generation of accurate and efficient multipolar precessing-spin effective-one-body waveforms for binary black holes
• Laying the foundation of the effective-one-body waveform models SEOBNRv5: improved accuracy and efficiency for spinning non-precessing binary black holes
• The Science of the Einstein Telescope
• New and Robust Gravitational-Waveform Model for High-Mass-Ratio Binary Neutron Star Systems with Dynamical Tidal Effects
• Effective-one-body waveforms for extreme-mass-ratio binaries: Consistency with second-order gravitational self-force quasicircular results and extension to nonprecessing spins and eccentricity
• Bayesian inference of multimessenger astrophysical data: Joint and coherent inference of gravitational waves and kilonovae
• Radiating Love: adiabatic tidal fluxes and modes up to next-to-next-to-leading post-Newtonian order
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Modeling matter(s) in SEOBNRv5THM: Generating fast and accurate effective-one-body waveforms for spin-aligned binary neutron stars
• Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
• Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants
• Comparison of neural network architectures for feature extraction from binary black hole merger waveforms
• Gravitational waves from eccentric binary neutron star mergers: Systematic biases and inadequacy of quasicircular templates
• Comparing next-generation detector configurations for high-redshift gravitational wave sources with neural posterior estimation
• Early Warning From Eccentric Compact Binaries: Template Initialization And Sub-dominant Mode Effects
• Auto-encoder model for faster generation of effective one-body gravitational waveform approximations