Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expen...

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Autores principales:	Scott E. Field, Chad R. Galley, Jan S. Hesthaven, Jason Kaye, Manuel Tiglio
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2014
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/21ea84a2b62f43bfbeb0e858a3ff47de
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spelling	oai:doaj.org-article:21ea84a2b62f43bfbeb0e858a3ff47de2021-12-02T11:47:28ZFast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models10.1103/PhysRevX.4.0310062160-3308https://doaj.org/article/21ea84a2b62f43bfbeb0e858a3ff47de2014-07-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.4.031006http://doi.org/10.1103/PhysRevX.4.031006https://doaj.org/toc/2160-3308We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform’s value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mc_{fit}) online operations, where c_{fit} denotes the fitting function operation count and, typically, m≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 10^{5}M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy. Surrogates built in this paper, as well as others, are available from GWSurrogate, a publicly available python package.Scott E. FieldChad R. GalleyJan S. HesthavenJason KayeManuel TiglioAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 4, Iss 3, p 031006 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Scott E. Field Chad R. Galley Jan S. Hesthaven Jason Kaye Manuel Tiglio Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
description	We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced order model in both parameter and physical dimensions that can be used as a surrogate for the true or fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant that is built for the fiducial waveform family. Third, a fit in the parameter dimension is performed for the waveform’s value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order O(mL+mc_{fit}) online operations, where c_{fit} denotes the fitting function operation count and, typically, m≪L. The result is a compact, computationally efficient, and accurate surrogate model that retains the original physics of the fiducial waveform family while also being fast to evaluate. We generate accurate surrogate models for effective-one-body waveforms of nonspinning binary black hole coalescences with durations as long as 10^{5}M, mass ratios from 1 to 10, and for multiple spherical harmonic modes. We find that these surrogates are more than 3 orders of magnitude faster to evaluate as compared to the cost of generating effective-one-body waveforms in standard ways. Surrogate model building for other waveform families and models follows the same steps and has the same low computational online scaling cost. For expensive numerical simulations of binary black hole coalescences, we thus anticipate extremely large speedups in generating new waveforms with a surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy. Surrogates built in this paper, as well as others, are available from GWSurrogate, a publicly available python package.
format	article
author	Scott E. Field Chad R. Galley Jan S. Hesthaven Jason Kaye Manuel Tiglio
author_facet	Scott E. Field Chad R. Galley Jan S. Hesthaven Jason Kaye Manuel Tiglio
author_sort	Scott E. Field
title	Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
title_short	Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
title_full	Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
title_fullStr	Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
title_full_unstemmed	Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models
title_sort	fast prediction and evaluation of gravitational waveforms using surrogate models
publisher	American Physical Society
publishDate	2014
url	https://doaj.org/article/21ea84a2b62f43bfbeb0e858a3ff47de
work_keys_str_mv	AT scottefield fastpredictionandevaluationofgravitationalwaveformsusingsurrogatemodels AT chadrgalley fastpredictionandevaluationofgravitationalwaveformsusingsurrogatemodels AT janshesthaven fastpredictionandevaluationofgravitationalwaveformsusingsurrogatemodels AT jasonkaye fastpredictionandevaluationofgravitationalwaveformsusingsurrogatemodels AT manueltiglio fastpredictionandevaluationofgravitationalwaveformsusingsurrogatemodels
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Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

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