2 citations · 2 across the 10 of their papers we have counts for
11 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…
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 +4
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), w…
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…
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…
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…
Generalized Steppingstone Sampling: Efficient marginal likelihood estimation in gravitational wave analysis of Pulsar Timing Array data
El Mehdi Zahraoui, Patricio Maturana-Russel, Willem van Straten +2
Globally, Pulsar Timing Array (PTA) experiments have revealed evidence supporting an existing gravitational wave background (GWB) signal in the PTA data set. Apart from acquiring m…
Variational inference for correlated gravitational wave detector network noise
Jianan Liu, Avi Vajpeyi, Renate Meyer +5
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…
Calibrating approximate Bayesian credible intervals of gravitational-wave parameters
Ruiting Mao, Jeong Eun Lee, Ollie Burke +3
Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW)…
Inference of proto-neutron star properties in core-collapse supernovae from a gravitational-wave detector network
T. Bruel, M-A. Bizouard, M. Obergaulinger +6
The next Galactic core-collapse supernova (CCSN) will be a unique opportunity to study within a fully multi-messenger approach the explosion mechanism responsible for the formation…