Inferring the core-collapse supernova explosion mechanism with three-dimensional gravitational-wave simulations

A detection of a core-collapse supernova signal with an Advanced LIGO and Virgo gravitational-wave detector network will allow us to measure astrophysical parameters of the source. In real advanced gravitational-wave detector data there are transient noise artifacts that may mimic a true gravitational-wave signal. In this paper, we outline a procedure implemented in the Supernova Model Evidence Extractor (SMEE) that determines if a core-collapse supernova signal candidate is a noise artefact, a rapidly-rotating core-collapse supernova signal, or a neutrino explosion mechanism core-collapse supernova signal. Further to this, we use the latest available three-dimensional gravitational-wave core-collapse supernova simulations, and we outline a new procedure for the rejection of background noise transients when only one detector is operational. We find the minimum SNR needed to detect all waveforms is reduced when using three-dimensional waveforms as signal models

Astrophysics with core-collapse supernova gravitational wave signals in the next generation of gravitational wave detectors

The next generation of gravitational wave detectors will improve the detection prospects for gravitational waves from core-collapse supernovae. The complex astrophysics involved in core-collapse supernovae pose a significant challenge to modeling such phenomena. The Supernova Model Evidence Extractor (SMEE) attempts to capture the main features of gravitational wave signals from core-collapse supernovae by using numerical relativity waveforms to create approximate models. These models can then be used to perform Bayesian model selection to determine if the targeted astrophysical feature is present in the gravitational wave signal. In this paper, we extend SMEE's model selection capabilities to include features in the gravitational wave signal that are associated with g-modes and the standing accretion shock instability. For the first time, we test SMEE's performance using simulated data for planned future detectors, such as the Einstein Telescope, Cosmic Explorer, and LIGO Voyager. Further to this, we show how the performance of SMEE is improved by creating models from the spectrograms of supernova waveforms instead of their timeseries waveforms that contain stochastic features. In third generation detector configurations, we find that about 50% of neutrino-driven simulations were detectable at 100 kpc, and 10% at 275 kpc. The explosion mechanism was correctly determined for all detected signals

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science 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 three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology

Model selection for gravitational-wave transient sources

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