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