P-33 Optimizing BayesWave

Jacob Willard, Andrews University

Abstract

A number of student researchers at Andrews University, lead by Dr. Tiffany Summerscales, are members of the Burst Data Analysis Group, which is part of the LIGO Scientific Collaboration. This group uses programs, such as BayesWave, to analyze gravitational wave detector data. BayesWave uses bayesian inference to distinguish between gravitational wave signals and noise in observed LIGO data. To improve the accuracy of Bayeswave, we test BayesWave using varying priors with mock-data injections.

Acknowledgments

Dr. Tiffany Summerscales

Start Date

3-3-2017 2:30 PM

End Date

3-3-2017 4:00 PM

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Mar 3rd, 2:30 PM Mar 3rd, 4:00 PM

P-33 Optimizing BayesWave

A number of student researchers at Andrews University, lead by Dr. Tiffany Summerscales, are members of the Burst Data Analysis Group, which is part of the LIGO Scientific Collaboration. This group uses programs, such as BayesWave, to analyze gravitational wave detector data. BayesWave uses bayesian inference to distinguish between gravitational wave signals and noise in observed LIGO data. To improve the accuracy of Bayeswave, we test BayesWave using varying priors with mock-data injections.