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.

21 pages, 12 figures

References in corpus (4)

• Magnetorotational Core-Collapse Supernovae in Three Dimensions
• The gravitational wave burst signal from core collapse of rotating stars
• 3D Collapse of Rotating Stellar Iron Cores in General Relativity including Deleptonization and a Nuclear Equation of State
• Rotating stellar core-collapse waveform decompositon: a Principal Component Analysis approach

Cited by in corpus (5)

• Parameter estimation with gravitational waves
• Gravitational wave asteroseismology with protoneutron stars
• Denoising of gravitational wave signals via dictionary learning algorithms
• Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission
• Computational Techniques for Parameter Estimation of Gravitational Wave Signals