From the 1 of 7 linked papers with an AI index.
7 papers
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…
Insights into the Pulsar Timing Array hypothesis space
El Mehdi Zahraoui, Patricio Maturana-Russel, Willem van Straten +2
We present novel insights into the pulsar-noise model space in pulsar timing array (PTA) experiments. Through a comparative analysis of the same Parkes PTA second data release obse…
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…
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…
Enhancing evidence estimation through informed probability density approximation
El Mehdi Zahraoui, Patricio Maturana-Russel, Avi Vajpeyi +3
We introduce the Morph approximation, a class of product approximations of probability densities that selects low-order disjoint parameter blocks by maximizing the sum of their tot…