LIGO Document T1500239-v6
- With the recent installation of the Advanced LIGO detectors in Hanford and Livingston, the sensitivity of detecting gravitational waves (GWs) has improved significantly. However, especially for unmodeled sources, it is still difficult to distinguish signals from instrumental noise with high confidence. The recent science and engineering runs suggest that apart from stationary Gaussian noise there are other instrumental artifacts, called ``glitches" that impose a challenge in recovering signals. The BayesWave Pipeline presents a novel way to approach this problem. BayesWave uses modern statistical methods to model the data as containing signals or glitches and attempts to calculate the evidence for each of the two competing models. In this project, we test the pipeline's performance in recovering injected signals added to initial LIGO data. We run BayesWave as a follow-up to coherent WaveBurst (cWB), which is the primary analysis pipeline for identifying burst signals in noisy data from the LIGO detectors, and we compare the efficiency for confident burst detection of cWB alone with that of the combined pipeline.
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