05 Dec 2019
Workshop on Machine Learning in Astronomy and Astrophysics
Seoul, South Korea
2020
Physical Review D
Gravitational wave astrophysics relies heavily on the use of matched filtering both to detect signals in noisy data from detectors, and to perform parameter estimation on those signals. Matched fil...
Project name:
heron
Dates: 2015-10-01 - Present
Project Status: Ongoing
Project Description
Understanding the waveform for a binary black hole coalescence is important for a number of data analysis tasks in gravitational wave astronomy, including parameter estimation and testing General Relativity. Producing precise waveforms is slow and computationally intensive, however. This project involves the development of accurate surrogate models which can be used in Bayesian inference.