Poster Title
P-23 Finding Optimal Input Parameters for BayesWave
Abstract
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 detection of un-modelled gravitational wave transients. In this project, we add binary black hole gravitational waveforms to LIGO noise with different combinations of parameters to find the best method of separating gravitational waves from noise and glitches. From the results we will calculate various statistical measures including confusion matrices and F1 scores for each parameter combination in order to determine which allows for the most accurate classification of gravitational wave transients.
Start Date
2-28-2020 2:30 PM
P-23 Finding Optimal Input Parameters for BayesWave
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 detection of un-modelled gravitational wave transients. In this project, we add binary black hole gravitational waveforms to LIGO noise with different combinations of parameters to find the best method of separating gravitational waves from noise and glitches. From the results we will calculate various statistical measures including confusion matrices and F1 scores for each parameter combination in order to determine which allows for the most accurate classification of gravitational wave transients.
Acknowledgments
J.N. Andrews Honors Scholar
Mentor: Tiffany Summerscales, Physics