Date of Award

4-23-2020

Document Type

Honors Thesis

Department

Engineering & Computer Science

First Advisor

Tiffany Z. Summerscales

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 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.

Subject Area

Gravitational waves; BayesWave; LIGO

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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