Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse

Abstract

We present a new multivariate regression model for analysis and parameter estimation of gravitational waves observed from well but not perfectly modeled sources such as core-collapse supernovae. Our approach is based on a principal component decomposition of simulated waveform catalogs. Instead of reconstructing waveforms by direct linear combination of physically meaningless principal components, we solve via least squares for the relationship that encodes the connection between chosen physical parameters and the principal component basis. Although our approach is linear, the waveforms' parameter dependence may be non-linear. For the case of gravitational waves from rotating core collapse, we show, using statistical hypothesis testing, that our method is capable of identifying the most important physical parameters that govern waveform morphology in the presence of simulated detector noise. We also demonstrate our method's ability to predict waveforms from a principal component basis given a set of physical progenitor parameters.

arXiv comment21 pages, 12 figures

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• Observing Gravitational Waves from Core-Collapse Supernovae in the Advanced Detector Era
• Equation of State Effects on Gravitational Waves from Rotating Core Collapse
• Parameter estimation with gravitational waves
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• Exploring Supernova Gravitational Waves with Machine Learning
• Ringdown mode amplitudes of precessing binary black holes
• Probing nuclear physics with supernova gravitational waves and machine learning
• Computational Techniques for Parameter Estimation of Gravitational Wave Signals