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

Probing intrinsic properties of short gamma-ray bursts with gravitational waves

Progenitors of short gamma-ray bursts are thought to be neutron stars coalescing with their companion black hole or neutron star, which are one of the main gravitational wave sources. We have devised a Bayesian framework for combining gamma-ray burst and gravitational wave information that allows us to probe short gamma-ray burst luminosities. We show that combined short gamma-ray burst and gravitational wave observations not only improve progenitor distance and inclination angle estimates, they also allow the isotropic luminosities of short gamma-ray bursts to be determined without the need for host galaxy or light-curve information. We characterise our approach by simulating 1000 joint short gamma-ray burst and gravitational wave detections by Advanced LIGO and Advanced Virgo. We show that ${\sim}90\%$ of the simulations have uncertainties on short gamma-ray burst isotropic luminosity estimates that are within a factor of 2 of the ideal scenario, where the distance is known exactly. Therefore, isotropic luminosities can be confidently determined for short gamma-ray bursts observed jointly with gravitational wave detected by Advanced LIGO and Advanced Virgo. Planned enhancements to Advanced LIGO will extend its range and likely produce several joint detections of short gamma-ray bursts and gravitational waves. Third-generation gravitational wave detectors will allow for isotropic luminosity estimates for the majority of the short gamma-ray burst population within a redshift of $z{\sim}1$

A Bayesian approach to multi-messenger astronomy: Identification of gravitational-wave host galaxies

We present a general framework for incorporating astrophysical information into Bayesian parameter estimation techniques used by gravitational wave data analysis to facilitate multi-messenger astronomy. Since the progenitors of transient gravitational wave events, such as compact binary coalescences, are likely to be associated with a host galaxy, improvements to the source sky location estimates through the use of host galaxy information are explored. To demonstrate how host galaxy properties can be included, we simulate a population of compact binary coalescences and show that for ~8.5% of simulations with in 200Mpc, the top ten most likely galaxies account for a ~50% of the total probability of hosting a gravitational wave source. The true gravitational wave source host galaxy is in the top ten galaxy candidates ~10% of the time. Furthermore, we show that by including host galaxy information, a better estimate of the inclination angle of a compact binary gravitational wave source can be obtained. We also demonstrate the flexibility of our method by incorporating the use of either B or K band into our analysis.Comment: 22 pages, 8 figures, accepted for publication in the Ap

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

A Precessing Numerical Relativity Waveform Surrogate Model for Binary Black Holes: A Gaussian Process Regression Approach

Gravitational wave astrophysics relies heavily on the use of matched filtering both to detect signals in noisy data from detectors, and to perform parameter estimation on those signals. Matched filtering relies upon prior knowledge of the signals expected to be produced by a range of astrophysical systems, such as binary black holes. These waveform signals can be computed using numerical relativity techniques, where the Einstein field equations are solved numerically, and the signal is extracted from the simulation. Numerical relativity simulations are, however, computationally expensive, leading to the need for a surrogate model which can predict waveform signals in regions of the physical parameter space which have not been probed directly by simulation. We present a method for producing such a surrogate using Gaussian process regression which is trained directly on waveforms generated by numerical relativity. This model returns not just a single interpolated value for the waveform at a new point, but a full posterior probability distribution on the predicted value. This model is therefore an ideal component in a Bayesian analysis framework, through which the uncertainty in the interpolation can be taken into account when performing parameter estimation of signals.Comment: 13 pages, with 7 figures. Accepted by Physical Review

Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach

We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a convolutional neural network can be used to detect and classify the gravitational wave signals buried in noise. For the waveforms used in the training of the convolutional neural network, our results suggest that a network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+, advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse supernovae within the Large and Small Magellanic Clouds, or a Galactic event if the explosion mechanism is the neutrino-driven mechanism. By testing the convolutional neural network with waveforms not used for training, we show that the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities are 70% and 93% respectively. All at false alarm probability equal to 10%

Trans-Ejecta High-Energy Neutrino Emission from Binary Neutron Star Mergers

The observations of a macronova/kilonova accompanied by gravitational waves from a binary neutron star merger (GW170817) confirmed that neutron star coalescences produce copious ejecta. The coincident gamma-ray detection implies the existence of a relativistic jet in this system. During the jet's propagation within the ejecta, high-energy photons and neutrinos can be produced. The photons are absorbed by the ejecta, while the neutrinos escape and can be detected. Here, we estimate such trans-ejecta neutrino emission, and discuss how neutrino observations could be used to differentiate between gamma-ray burst emission scenarios. We find that neutrinos from the internal shocks inside the ejecta may be detectable by IceCube within a few years of operation, and will likely be detected with IceCube-Gen2. The neutrino signals coincident with gravitational waves would enable us to reveal the physical quantities of the choked jets even without electromagnetic signals.Comment: 12 pages, 5 figures, 2 tables, accepted for publication in PR

Comparing short gamma-ray burst jet structure models

A structured gamma-ray burst (GRB) jet could explain the dimness of the prompt emission observed from GRB 170817A, but the exact form of this structure is still ambiguous. However, with the promise of future joint gravitational wave (GW) and GRB observations, we shall be able to examine populations of binary neutron star (BNS) mergers rather than on a case-by-case basis. We present an analysis that considers GW triggered BNS events both with and without short GRB counterparts assuming that events without a counterpart were observed off-axis. This allows for Bayes factors to be calculated to compare different jet structure models. We perform model comparison between a Gaussian and power-law apparent jet structure on simulated data to demonstrate that the correct model can be distinguished with a log Bayes factor of >5 after fewer than 100 events. Constraints on the apparent structure jet model parameters are also made. After 25(100) events the angular width of the core of a power-law jet structure can be constrained within a 90% credible interval of width ~9.1(4.4)°, and the outer beaming angle to be within ~19.9(8.5)°. Similarly, we show the width of a Gaussian jet structure to be constrained to ~2.8(1.6)°