From the 1 of 10 linked papers with an AI index.
10 papers
Variational Bayesian Inference for the Spectral Structure of LISA Noise
Jianan Liu, Avi Vajpeyi, Renate Meyer +2
Estimating spectral density matrices for future space-based gravitational-wave detectors such as LISA is challenging due to the long duration of the data and the correlated instrum…
Bayesian P-spline recovery of stochastic gravitational-wave backgrounds in LISA
Nazeela Aimen, Patricio Maturana-Russel, Avi Vajpeyi +2
The detection of a stochastic gravitational-wave background (SGWB) is a primary science objective for the Laser Interferometer Space Antenna (LISA). However, extracting these signa…
Bayesian nonparametric estimation of correlated gravitational wave detector network noise using matrix-gamma process priors
Yixuan Liu, Renate Meyer, Nelson Christensen +5
The paper introduces a Bayesian nonparametric method that directly estimates the correlated noise spectral density matrix of future gravitational‑wave detector networks using matri…
An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis
Avi Vajpeyi, Giorgio Mentasti, Quentin Baghi +2
The Wilson-Daubechies-Meyer (WDM) time-frequency transform has been widely used in gravitational-wave astronomy, yet a self-contained, mathematically explicit reference for practit…
Multivariate Bayesian P-spline estimation of spectral density matrices, with application to LISA TDI noise
Avi Vajpeyi, Renate Meyer, Patricio Maturana-Russel +1
We present a Bayesian P-spline method for estimating the frequency-dependent cross-spectral density matrix of stationary multivariate time series. The inverse spectral matrix is pa…
Bayesian power spectral density estimation for LISA noise based on penalized splines with a parametric boost
Nazeela Aimen, Patricio Maturana-Russel, Avi Vajpeyi +2
Flexible and accurate noise characterization is crucial for the precise estimation of gravitational-wave parameters. We introduce a Bayesian method for estimating the power spectra…