203 citations · 736 across the 19 of their papers we have counts for
7 papers · 1 filter
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
Alicja Polanska, Thibeau Wouters, Peter T. H. Pang +2
We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrat…
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Ronan Legin, Maximiliano Isi, Kaze W. K. Wong +2
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled o…
Fast gravitational wave parameter estimation without compromises
Kaze W. K. Wong, Maximiliano Isi, Thomas D. P. Edwards
We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically-di…
ripple: Differentiable and Hardware-Accelerated Waveforms for Gravitational Wave Data Analysis
Thomas D. P. Edwards, Kaze W. K. Wong, Kelvin K. H. Lam +4
We propose the use of automatic differentiation through the programming framework jax for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. First…
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
David Ruhe, Kaze Wong, Miles Cranmer +1
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first dem…
flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey
flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. A…
Automated discovery of interpretable gravitational-wave population models
Kaze W. K Wong, Miles Cranmer
We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible…