Machine-learning non-stationary noise out of gravitational wave detectors

no code implementations • 20 Nov 2019 • Gabriele Vajente, Yiwen Huang, Maximiliano Isi, Jenne C. Driggers, Jeffrey S. Kissel, Marek J. Szczepanczyk, Salvatore Vitale

Signal extraction out of background noise is a common challenge in high precision physics experiments, where the measurement output is often a continuous data stream.

BIG-bench Machine Learning

Impact of Bayesian prior on the characterization of binary black hole coalescences

no code implementations • 14 Jul 2017 • Salvatore Vitale, Davide Gerosa, Carl-Johan Haster, Katerina Chatziioannou, Aaron Zimmerman

In a regime where data are only mildly informative, prior choices can play a significant role in Bayesian statistical inference, potentially affecting the inferred physics.

General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena

Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

no code implementations • 25 Sep 2014 • John Veitch, Vivien Raymond, Benjamin Farr, Will M. Farr, Philip Graff, Salvatore Vitale, Ben Aylott, Kent Blackburn, Nelson Christensen, Michael Coughlin, Walter Del Pozzo, Farhan Feroz, Jonathan Gair, Carl-Johan Haster, Vicky Kalogera, Tyson Littenberg, Ilya Mandel, Richard O'Shaughnessy, Matthew Pitkin, Carl Rodriguez, Christian Röver, Trevor Sidery, Rory Smith, Marc Van Der Sluys, Alberto Vecchio, Will Vousden, Leslie Wade

We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors.

General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics