MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

1 code implementation • 22 Sep 2022 • Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz, He Wang, Shichao Wu, Zong-Kuan Guo, Zhoujian Cao, Zhixiang Ren, Paraskevi Nousi, Nikolaos Stergioulas, Panagiotis Iosif, Alexandra E. Koloniari, Anastasios Tefas, Nikolaos Passalis, Francesco Salemi, Gabriele Vedovato, Sergey Klimenko, Tanmaya Mishra, Bernd Brügmann, Elena Cuoco, E. A. Huerta, Chris Messenger, Frank Ohme

The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes.

Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

2 code implementations • 13 Sep 2019 • Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, Roderick Murray-Smith

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year.

Astronomy Bayesian Inference

Matching matched filtering with deep networks in gravitational-wave astronomy

1 code implementation • 17 Dec 2017 • Hunter Gabbard, Michael Williams, Fergus Hayes, Chris Messenger

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 signals.

Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology