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Found 6 papers, 4 papers with code
Reduced-order modeling with artificial neurons for gravitational-wave inference
Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images.
Constraining the parameters of GW150914 & GW170104 with numerical relativity surrogates
In this paper we demonstrate the viability of these surrogate models as reliable parameter estimation tools, and show that within a fully Bayesian framework surrogates can help us extract more information from gravitational wave observations than traditional models.
General Relativity and Quantum Cosmology 83C57, 83C35 J.2
A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers
A generic, non-eccentric binary black hole (BBH) system emits gravitational waves (GWs) that are completely described by 7 intrinsic parameters: the black hole spin vectors and the ratio of their masses.
General Relativity and Quantum Cosmology
A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers
We present the first surrogate model for gravitational waveforms from the coalescence of precessing binary black holes.
General Relativity and Quantum Cosmology
Fast and efficient evaluation of gravitational waveforms via reduced-order spline interpolation
To handle these challenges, we present a simple and efficient method to significantly \emph{compress} the original waveform data sets while accurately reproducing the original data via spline interpolation.
General Relativity and Quantum Cosmology
Fast prediction and evaluation of gravitational waveforms using surrogate models
Our approach is based on three offline steps resulting in an accurate reduced-order model that can be used as a surrogate for the true/fiducial waveform family.
General Relativity and Quantum Cosmology Computational Engineering, Finance, and Science
