Fast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Models

dc.contributor.authorField, Scott E.
dc.contributor.authorGalley, Chad R.
dc.contributor.authorHesthaven, Jan S.
dc.contributor.authorKaye, Jason
dc.contributor.authorTiglio, Manuel
dc.descriptionFunding for Open Access provided by the UMD Libraries Open Access Publishing Fund.
dc.description.abstractWe 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+mcfit) online operations, where cfit 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 105M, 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.en_US
dc.description.sponsorshipThis 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.en_US
dc.identifier.citationScott Field, Chad Galley, Jan Hesthaven, Jason Kaye, and Manuel Tiglio. "Fast prediction and evaluation of gravitational waveforms using surrogate models" (accepted to PRX). arXiv: gr-qc:1308.3565 doi:
dc.publisherAmerican Physical Societyen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectComputational Physicsen_US
dc.titleFast Prediction and Evaluation of Gravitational Waveforms Using Surrogate Modelsen_US


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