250 citations · 440 across the 6 of their papers we have counts for
7 papers
Normalizing flows for density estimation in multi-detector gravitational-wave searches
Sam Insley, Michael J. Williams, Rahul Dhurkunde +1
Identifying compact binary coalescences buried within the non-Gaussian and non-stationary data taken by gravitational-wave interferometers requires sophisticated search pipelines,…
Inference with finite time series II: the window strikes back
Colm Talbot, Sylvia Biscoveanu, Aaron Zimmerman +9
Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-…
Unpacking merger jets: a Bayesian analysis of GW170817, GW190425 and electromagnetic observations of short gamma-ray bursts
Fergus Hayes, Ik Siong Heng, Gavin Lamb +3
We present a novel fully Bayesian analysis to constrain short gamma-ray burst jet structures associated with cocoon, wide-angle and simple top-hat jet models, as well as the binary…
Importance nested sampling with normalising flows
Michael J. Williams, John Veitch, Chris Messenger
We present an improved version of the nested sampling algorithm nessai in which the core algorithm is modified to use importance weights. In the modified algorithm, samples are dra…
A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties
En-Tzu Lin, Fergus Hayes, Gavin P. Lamb +5
In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical sy…
Nested Sampling with Normalising Flows for Gravitational-Wave Inference
Michael J. Williams, John Veitch, Chris Messenger
We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our…
Matching matched filtering with deep networks in gravitational-wave astronomy
Hunter Gabbard, Michael Williams, Fergus Hayes +1
We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signa…