P-21 Parameterization of BayesWave for Analysis of Gravitational Waves Caused by Inspiraling Black Holes
Abstract
Gravitational waves are ripples in the fabric of spacetime caused by large gravitational events. LIGO was built to detect gravitational wave signals. Programs such as BayesWave are designed to analyze signals detected by LIGO. By analyzing these signals, it is possible to gain a greater understanding of gravitational waves and their sources, which include binary stars, supernovas, and black holes. However, BayesWave remains unoptimized in many areas. By examining BayesWave’s analysis of an injected gravitational wave signal while systematically varying several pre-set parameters, this research aims to optimize BayesWave for the analysis of gravitational waves caused by inspiraling black holes.
Location
Buller Hall
Start Date
2-26-2016 2:30 PM
End Date
2-26-2016 4:00 PM
P-21 Parameterization of BayesWave for Analysis of Gravitational Waves Caused by Inspiraling Black Holes
Buller Hall
Gravitational waves are ripples in the fabric of spacetime caused by large gravitational events. LIGO was built to detect gravitational wave signals. Programs such as BayesWave are designed to analyze signals detected by LIGO. By analyzing these signals, it is possible to gain a greater understanding of gravitational waves and their sources, which include binary stars, supernovas, and black holes. However, BayesWave remains unoptimized in many areas. By examining BayesWave’s analysis of an injected gravitational wave signal while systematically varying several pre-set parameters, this research aims to optimize BayesWave for the analysis of gravitational waves caused by inspiraling black holes.
Acknowledgments
Dr. Tiffany Summerscales