arXiv subjects

M. J. Williams

Publications and source records attributed to M. J. Williams.

2 recordsLinked to original sources

arXiv2021

Bayesian inference for compact binary coalescences with BILBY: Validation and application to the first LIGO--Virgo gravitational-wave transient catalogue

Gravitational waves provide a unique tool for observational astronomy. While the first LIGO--Virgo catalogue of gravitational-wave transients (GWTC-1) contains eleven signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the eleven GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.

astro-ph.IM↗

arXiv2021

Generalised gravitational burst generation with Generative Adversarial Networks

We introduce the use of conditional generative adversarial networks forgeneralised gravitational wave burst generation in the time domain.Generativeadversarial networks are generative machine learning models that produce new databased on the features of the training data set. We condition the network on fiveclasses of time-series signals that are often used to characterise gravitational waveburst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binaryblack hole merger. We show that the model can replicate the features of these standardsignal classes and, in addition, produce generalised burst signals through interpolationand class mixing. We also present an example application where a convolutional neuralnetwork classifier is trained on burst signals generated by our conditional generativeadversarial network. We show that a convolutional neural network classifier trainedonly on the standard five signal classes has a poorer detection efficiency than aconvolutional neural network classifier trained on a population of generalised burstsignals drawn from the combined signal class space.

astro-ph.IM↗