| Summary: | A hundred years after Einstein predicted the existence of gravitational waves, the first direct detection was made from gravitational waves emitted by a binary black hole system. Other potential sources for an advanced gravitational-wave detector network include core-collapse supernovae. Due to complicated simulations of the physics involved in core-collapse supernovae, the exact waveform of a core-collapse supernova signal is unknown. A detection of a core-collapse supernova signal is challenging, as noise of non-astrophysical origin contaminates the science data taken by the advanced detectors. Noise transients in the detectors limit the false alarm rate of astrophysical detections, and could potentially mimic a core-collapse supernova signal. They can reduce the duty cycle of the detectors, which is particularly harmful for core-collapse supernovae detections due to their low event rate. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors during observing runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present a Bayesian model selection algorithm designed for the automatic classification of noise transients in advanced gravitational-wave detectors. The algorithm is tested on simulated data sets and real non-Gaussian, non-stationary Advanced LIGO noise, and we demonstrate the ability to automatically classify transients by frequency, SNR and waveform morphology. A classification of noise transients as data is taken can lead to an improvement in data quality during an observing run and determine their origin. In this thesis, we show how Bayesian model selection can be used to determine if a core-collapse supernova candidate gravitational-wave signal is a noise transient, a core-collapse supernova signal or other astrophysical transient. If the signal is a core-collapse supernova detection, we show how the core-collapse supernova explosion mechanism can be determined using a combination of principal component analysis and Bayesian model selection. We use the latest three-dimensional simulations of gravitational-wave signals from core-collapse supernovae exploding via neutrino-driven convection and rapidly-rotating core-collapse. We show that with an advanced detector network, we can determine if the core-collapse supernova explosion mechanism is neutrino-driven convection for sources in our Galaxy, and rapidly-rotating core collapse for sources out to the Large Magellanic Cloud.
|
|---|
