Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors
Abstract
This paper addresses the important problem of estimating the noise spectral density of next-generation gravitational-wave detectors, such as LISA and the Einstein Telescope (ET), where cross-channel correlations must be accounted for to avoid biased parameter estimation of gravitational-wave signals. Unlike approaches that estimate test-mass and optical-metrology-system noise separately at the single-link level and then map them to the Time-Delay Interferometry (TDI) channels through known transfer functions, we develop a Bayesian nonparametric method that directly estimates the spectral density matrix of the XYZ channels, thereby accommodating additional sources of uncertainty. Our approach combines a flexible matrix-gamma process prior on the matrix-valued coefficients of a Bernstein polynomial basis expansion with a blocked multivariate Whittle likelihood. The prior guarantees Hermitian positive definiteness of the spectral estimate at every frequency. To avoid reversible-jump methods, we use an adaptive Markov chain Monte Carlo (MCMC) algorithm for posterior sampling. The proposed framework can also be used to correct misspecified parametric noise models. Results from a simulation study and simulated correlated-noise data for both LISA and ET demonstrate the effectiveness of the proposed method.
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BibTeX
@misc{liu2026bayesian,
title = {{Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors}},
author = {Yixuan Liu and Renate Meyer and Nelson Christensen and Jeung Eun Lee and Jianan Liu and Patricio Maturana-Russel and Avi Vajpeyi},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.26619},
eprint = {2607.26619},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05733690v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors
AU - Yixuan Liu
AU - Renate Meyer
AU - Nelson Christensen
AU - Jeung Eun Lee
AU - Jianan Liu
AU - Patricio Maturana-Russel
AU - Avi Vajpeyi
PY - 2026
DA - 2026/09/01
PB - HAL
DO - 10.48550/arxiv.2607.26619
UR - https://hal.science/hal-05733690v1
AB - This paper addresses the important problem of estimating the noise spectral density of next-generation gravitational-wave detectors, such as LISA and the Einstein Telescope (ET), where cross-channel correlations must be accounted for to avoid biased parameter estimation of gravitational-wave signals. Unlike approaches that estimate test-mass and optical-metrology-system noise separately at the single-link level and then map them to the Time-Delay Interferometry (TDI) channels through known transfer functions, we develop a Bayesian nonparametric method that directly estimates the spectral density matrix of the XYZ channels, thereby accommodating additional sources of uncertainty. Our approach combines a flexible matrix-gamma process prior on the matrix-valued coefficients of a Bernstein polynomial basis expansion with a blocked multivariate Whittle likelihood. The prior guarantees Hermitian positive definiteness of the spectral estimate at every frequency. To avoid reversible-jump methods, we use an adaptive Markov chain Monte Carlo (MCMC) algorithm for posterior sampling. The proposed framework can also be used to correct misspecified parametric noise models. Results from a simulation study and simulated correlated-noise data for both LISA and ET demonstrate the effectiveness of the proposed method.
ER -