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Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

Field, Scott E. and Galley, Chad R. and Hesthaven, Jan S. and Kaye, Jason and Tiglio, Manuel (2014) Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models. Physical Review X, 4 (3). Art. No. 031006. ISSN 2160-3308. doi:10.1103/PhysRevX.4.031006.

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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 10^5M, 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.

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Additional Information:© 2014 American Physical Society. Received 20 August 2013; revised manuscript received 27 February 2014; published 14 July 2014. We thank Frank Herrmann and Evan Ochsner for help during this project, including some software tools, as well as Yi Pan, Alessandra Buonnano, and Collin Capano for helpful discussions about the EOB model and its generation using the LAL code.We thank Michael Pürrer for comments on a previous version of the paper. This work was supported in part by NSF Grants No. PHY-1208861, No. PHY-1316424, and No. PHY-1005632 to the University of Maryland and by NSF Grant No. PHY-1068881 and CAREER Grant No. PHY-0956189 to the California Institute of Technology.
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Subject Keywords:Astrophysics, Computational Physics, Gravitation
Issue or Number:3
Record Number:CaltechAUTHORS:20140926-091550606
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Official Citation:Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models Scott E. Field, Chad R. Galley, Jan S. Hesthaven, Jason Kaye, and Manuel Tiglio Phys. Rev. X 4, 031006 (2014) – Published 14 July 2014
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:50058
Deposited By: Ruth Sustaita
Deposited On:26 Sep 2014 16:49
Last Modified:10 Nov 2021 18:51

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