54 citations · 106 across the 12 of their papers we have counts for
13 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…
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…
LIGO Core-Collapse Supernova Detection using Convolution Neural Networks
Zhicheng Pan, El Mehdi Zahraoui, Guillermo Cabrera-Guerrero +1
Core-Collapse Supernovae (CCSNe) remain a critical focus in the search for gravitational waves (GWs) in modern astronomy. Their detection and subsequent analysis will enhance our u…
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…
Inference of proto-neutron star properties from gravitational-wave data in core-collapse supernovae
Marie-Anne Bizouard, Patricio Maturana-Russel, Alejandro Torres-Forné +5
The eventual detection of gravitational waves from core-collapse supernovae (CCSN) will help improve our current understanding of the explosion mechanism of massive stars. The stoc…
Computational Techniques for Parameter Estimation of Gravitational Wave Signals
Renate Meyer, Matthew C. Edwards, Patricio Maturana-Russel +1
Since the very first detection of gravitational waves from the coalescence of two black holes in 2015, Bayesian statistical methods have been routinely applied by LIGO and Virgo to…
Identifying and Addressing Nonstationary LISA Noise
Matthew C. Edwards, Patricio Maturana-Russel, Renate Meyer +3
We anticipate noise from the Laser Interferometer Space Antenna (LISA) will exhibit nonstationarities throughout the duration of its mission due to factors such as antenna repointi…
Bayesian spectral density estimation using P-splines with quantile-based knot placement
Patricio Maturana-Russel, Renate Meyer
This article proposes a Bayesian approach to estimating the spectral density of a stationary time series using a prior based on a mixture of P-spline distributions. Our proposal is…