73 research outputs found
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 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
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%
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)°
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
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