Date of Award
Engineering & Computer Science
Tiffany Z. Summerscales
This project involves data analysis for LIGO with the goal of finding optimal input parameters for the BayesWave analysis pipeline, which is an algorithm for the detection of unmodelled gravitational wave transients. To test the BayesWave pipeline, we add binary black hole gravitational waveforms to LIGO noise, and run BayesWave with different combinations of parameters on the resulting signal data to find the best method of separating gravitational waves from noise and glitches. From the results, we calculate various statistical measures for each parameter combination in order to determine which allows for the most accurate classification of gravitational wave transients.
Rook, Kelsey M., "Finding Optimal Input Parameters for BayesWave" (2020). Honors Theses. 237.
Gravitational waves; BayesWave; LIGO
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