Bayesian power spectral density estimation for LISA noise based on penalized splines with a parametric boost
Abstract
Flexible and accurate noise characterization is crucial for the precise estimation of gravitational-wave parameters. We introduce a Bayesian method for estimating the power spectral density (PSD) of long, stationary time series, explicitly tailored for LISA data analysis. Our approach models the PSD as the geometric mean of a parametric and a nonparametric component, combining the knowledge from parametric models with the flexibility to capture deviations from theoretical expectations. The nonparametric component is expressed by a mixture of penalized B-splines. Adaptive, data-driven knot placement, performed once at initialization, removes the need for reversible-jump Markov chain Monte Carlo, while hierarchical roughness-penalty priors prevent overfitting. Validation on simulated autoregressive AR(4) data demonstrates estimator consistency and shows that well-matched parametric components reduce the integrated absolute error compared to an uninformative baseline, requiring fewer spline knots to achieve comparable accuracy. Applied to one year of simulated LISA X-channel (univariate) noise, our method achieves relative integrated absolute errors of , making it suitable for iterative analysis pipelines and multi-year mission data sets.
Cite
@article{arxiv.2510.00533,
title = {Bayesian power spectral density estimation for LISA noise based on penalized splines with a parametric boost},
author = {Nazeela Aimen and Patricio Maturana-Russel and Avi Vajpeyi and Nelson Christensen and Renate Meyer},
journal= {arXiv preprint arXiv:2510.00533},
year = {2026}
}