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

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arxiv 2210.15684 v2 pith:DLFDBR77 submitted 2022-10-27 gr-qc

Jacopo Tissino , Gregorio Carullo , Matteo Breschi , Rossella Gamba , Stefano Schmidt , Sebastiano Bernuzzi This is my paper · ORCID
classification gr-qc
keywords accuracyeffective-one-bodygenerationaccuratebinaryestimationimprovementmlgw-bns
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

    gr-qc 2026-07 accept novelty 6.0 of 10

    A piecewise MLP surrogate emulates NRSur7dq4 over its full domain at NR-faithful accuracy with ~1 ms GPU latency and a fully differentiable JAX likelihood pipeline.

  2. Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

    gr-qc 2025-01 conditional novelty 6.0 of 10

    A systematically optimized neural network surrogate for black-hole merger remnant properties, NRSur7dq4Remnant_NN, matches the accuracy of the GPR-based NRSur7dq4Remnant while evaluating up to 8 times faster on a CPU ...

  3. Data-driven approach for extracting tidal information from neutron star binary mergers observed with the Einstein Telescope

    gr-qc 2025-01 conditional novelty 6.0 of 10

    A simulation study showing that the tidal phase of neutron-star mergers can be inferred directly from Einstein Telescope data by fitting six free polynomial coefficients and combining posteriors across events.

  4. Revisiting GW150914 with a non-planar, eccentric waveform model

    gr-qc 2025-05 conditional novelty 5.0 of 10

    Using a waveform model that includes both eccentricity and spin precession, the authors confirm GW150914 was a quasi-circular, slowly spinning black hole merger, with eccentricity below 0.08 at 15 Hz.

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