Bayesian semiparametric power spectral density estimation with applications in gravitational wave data analysis
Abstract
The standard noise model in gravitational wave (GW) data analysis assumes detector noise is stationary and Gaussian distributed, with a known power spectral density (PSD) that is usually estimated using clean offsource data. Real GW data often depart from these assumptions, and misspecified parametric models of the PSD could result in misleading inferences. We propose a Bayesian semiparametric approach to improve this. We use a nonparametric Bernstein polynomial prior on the PSD, with weights attained via a Dirichlet process distribution, and update this using the Whittle likelihood. Posterior samples are obtained using a blocked MetropoliswithinGibbs sampler. We simultaneously estimate the reconstruction parameters of a rotating core collapse supernova GW burst that has been embedded in simulated Advanced LIGO noise. We also discuss an approach to deal with nonstationary data by breaking longer data streams into smaller and locally stationary components.
 Publication:

Physical Review D
 Pub Date:
 September 2015
 DOI:
 10.1103/PhysRevD.92.064011
 arXiv:
 arXiv:1506.00185
 Bibcode:
 2015PhRvD..92f4011E
 Keywords:

 04.30.w;
 02.50.r;
 05.45.Tp;
 97.60.Bw;
 Gravitational waves: theory;
 Probability theory stochastic processes and statistics;
 Time series analysis;
 Supernovae;
 General Relativity and Quantum Cosmology;
 Physics  Data Analysis;
 Statistics and Probability;
 Statistics  Applications
 EPrint:
 15 pages, 15 figures