Variational Inference for Correlated Gravitational Wave Detector Network Noise
Files
Date
Authors
Liu, Jianan
Vajpeyi, Avi
Meyer, Renate
Janssens, Kamiel
Lee, Jeung Eun
Maturana-Russel, Patricio
Christensen, Nelson
Liu, Yixuan
Supervisor
Item type
Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv
Abstract
Gravitational wave detectors like the Einstein Telescope and LISA generate long multivariate time series, which pose significant challenges in spectral density estimation due to a number of overlapping signals as well as the presence of correlated noise. Addressing both issues is crucial for accurately interpreting the signals detected by these instruments. This paper presents an application of a variational inference spectral density estimation method specifically tailored for dealing with correlated noise in the data. It is flexible in that it does not rely on any specific parametric form for the multivariate spectral density. The method employs a blocked Whittle likelihood approximation for stationary time series and utilizes the Cholesky decomposition of the inverse spectral density matrix to ensure a positive definite estimator. A discounted regularized horseshoe prior is applied to the spline coefficients of each Cholesky factor, and the posterior distribution is computed using a stochastic gradient variational Bayes approach. This method is particularly effective in addressing correlated noise, a significant challenge in the analysis of multivariate data from co-located detectors. The method is demonstrated by analyzing 2000 seconds of simulated Einstein Telescope noise, which shows its ability to produce accurate spectral density estimates and quantify coherence between time series components. This makes it a powerful tool for analyzing correlated noise in gravitational wave data.Description
Keywords
49 Mathematical Sciences, 4905 Statistics, 51 Physical Sciences
Source
arXiv. Retrieved from: https://arxiv.org/abs/2409.13224v1