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

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Standard

Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning. / Tissino, Jacopo; Carullo, Gregorio; Breschi, Matteo; Gamba, Rossella; Schmidt, Stefano; Bernuzzi, Sebastiano.

I: Physical Review D, Bind 107, Nr. 8, 084037, 25.04.2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Tissino, J, Carullo, G, Breschi, M, Gamba, R, Schmidt, S & Bernuzzi, S 2023, 'Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning', Physical Review D, bind 107, nr. 8, 084037. https://doi.org/10.1103/PhysRevD.107.084037

APA

Tissino, J., Carullo, G., Breschi, M., Gamba, R., Schmidt, S., & Bernuzzi, S. (2023). Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning. Physical Review D, 107(8), [084037]. https://doi.org/10.1103/PhysRevD.107.084037

Vancouver

Tissino J, Carullo G, Breschi M, Gamba R, Schmidt S, Bernuzzi S. Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning. Physical Review D. 2023 apr. 25;107(8). 084037. https://doi.org/10.1103/PhysRevD.107.084037

Author

Tissino, Jacopo ; Carullo, Gregorio ; Breschi, Matteo ; Gamba, Rossella ; Schmidt, Stefano ; Bernuzzi, Sebastiano. / Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning. I: Physical Review D. 2023 ; Bind 107, Nr. 8.

Bibtex

@article{0b524cd90fc84d76a52ee49a5c6e4776,
title = "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 similar to 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.",
author = "Jacopo Tissino and Gregorio Carullo and Matteo Breschi and Rossella Gamba and Stefano Schmidt and Sebastiano Bernuzzi",
year = "2023",
month = apr,
day = "25",
doi = "10.1103/PhysRevD.107.084037",
language = "English",
volume = "107",
journal = "Physical Review D",
issn = "2470-0010",
publisher = "American Physical Society",
number = "8",

}

RIS

TY - JOUR

T1 - Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning

AU - Tissino, Jacopo

AU - Carullo, Gregorio

AU - Breschi, Matteo

AU - Gamba, Rossella

AU - Schmidt, Stefano

AU - Bernuzzi, Sebastiano

PY - 2023/4/25

Y1 - 2023/4/25

N2 - 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 similar to 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.

AB - 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 similar to 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.

U2 - 10.1103/PhysRevD.107.084037

DO - 10.1103/PhysRevD.107.084037

M3 - Journal article

VL - 107

JO - Physical Review D

JF - Physical Review D

SN - 2470-0010

IS - 8

M1 - 084037

ER -

ID: 347792947