arXiv subjects

Jade Powell

Publications and source records attributed to Jade Powell.

34 records · Page 2Linked to original sources

arXiv2021

Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10-25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions and models that form black holes a few seconds after the core bounce.

astro-ph.HE↗

arXiv2021

The final core collapse of pulsational pair instability supernovae

We present 3D core-collapse supernova simulations of massive Pop-III progenitor stars at the transition to the pulsational pair instability regime. We simulate two progenitor models with initial masses of $85\,\mathrm{M}_{\odot}$ and $100\,\mathrm{M}_\odot$ with the LS220, SFHo, and SFHx equations of state. The $85\,\mathrm{M}_{\odot}$ progenitor experiences a pair instability pulse coincident with core collapse, whereas the $100\,\mathrm{M}_{\odot}$ progenitor has already gone through a sequence of four pulses $1\mathord,500$ years before collapse in which it ejected its H and He envelope. The $85\,\mathrm{M}_{\odot}$ models experience shock revival and then delayed collapse to a black hole (BH) due to ongoing accretion within hundreds of milliseconds. The diagnostic energy of the incipient explosion reaches up to $2.7\times10^{51}\,\mathrm{erg}$ in the SFHx model. Due to the high binding energy of the metal core, BH collapse by fallback is eventually unavoidable, but partial mass ejection may be possible. The $100\,\mathrm{M}_\odot$ models have not achieved shock revival or undergone BH collapse by the end of the simulation. All models exhibit relatively strong gravitational-wave emission both in the high-frequency g-mode emission band and at low frequencies. The SFHx and SFHo models show clear emission from the standing accretion shock instability. For our models, we estimate maximum detection distances of up to $\mathord{\sim}46\,\mathrm{kpc}$ with LIGO and $\mathord{\sim} 850\,\mathrm{kpc}$ with Cosmic Explorer.

astro-ph.HE↗

arXiv2020

Enhancing Gravitational-Wave Science with Machine Learning

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.

astro-ph.HE↗

arXiv2020

Three-dimensional core-collapse supernova simulations of massive and rotating progenitors

We present three-dimensional simulations of the core-collapse of massive rotating and non-rotating progenitors performed with the general relativistic neutrino hydrodynamics code CoCoNuT-FMT and analyse their explosion properties and gravitational-wave signals. The progenitor models include Wolf-Rayet stars with initial helium star masses of $39\,M_{\odot}$ and $20\,M_{\odot}$, and an $18\,M_{\odot}$ red supergiant. The $39\,M_{\odot}$ model is a rapid rotator, whereas the two other progenitors are non-rotating. Both Wolf-Rayet models produce healthy neutrino-driven explosions, whereas the red supergiant model fails to explode. By the end of the simulations, the explosion energies have already reached $1.1\times 10^{51}\,\mathrm{erg}$ and $0.6\times 10^{51}\,\mathrm{erg}$ for the $39\,M_{\odot}$ and $20\,M_{\odot}$ model, respectively. The explosions produce neutron stars of relatively high mass, but with modest kicks. Due to the alignment of the bipolar explosion geometry with the rotation axis, there is a relatively small misalignment of $30^\circ$ between the spin and the kick in the $39\,M_{\odot}$ model. In terms of gravitational-wave signals, the massive and rapidly rotating $39\,M_{\odot}$ progenitor stands out by large gravitational-wave amplitudes that would make it detectable out to almost 2 Mpc by the Einstein Telescope. For this model, we find that rotation significantly changes the dependence of the characteristic gravitational-wave frequency of the f-mode on the proto-neutron star parameters compared to the non-rotating case. The other two progenitors have considerably smaller detection distances, despite significant low-frequency emission in the most sensitive frequency band of current gravitational-wave detectors due to the standing accretion shock instability in the $18\,M_{\odot}$ model.

astro-ph.HE↗

arXiv2020

Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification

We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection Filter (WDF). We employ both a 1-D CNN search using time series gravitational-wave data as input, and a 2-D CNN search with time-frequency representation of the data as input. To test the accuracies of our 1-D and 2-D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven core-collapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over 95% for both 1-D and 2-D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.

gr-qc↗

arXiv2019

The impact of pair-instability mass loss on the binary black hole mass distribution

A population of binary black hole mergers has now been observed in gravitational waves by Advanced LIGO and Virgo. The masses of these black holes appear to show evidence for a pile-up between $30$--$45$ $M_\odot$ and a cut-off above $\sim 45$ $M_\odot$. One possible explanation for such a pile-up and subsequent cut-off are pulsational pair-instability supernovae (PPISNe) and pair-instability supernovae (PISNe) in massive stars. We investigate the plausibility of this explanation in the context of isolated massive binaries. We study a population of massive binaries using the rapid population synthesis software COMPAS, incorporating models for PPISNe and PISNe. Our models predict a maximum black hole mass of $40$ $M_\odot$. We expect $\sim 10$\% of all binary black hole mergers at redshift z = 0 will include at least one component that went through a PPISN (with mass $30$--$40$ $M_\odot$), constituting $\sim 20$--$50$\% of binary black hole mergers observed during the first two observing runs of Advanced LIGO and Virgo. Empirical models based on fitting the gravitational-wave mass measurements to a combination of a power law and a Gaussian find a fraction too large to be associated with PPISNe in our models. The rates of PPISNe and PISNe track the low metallicity star formation rate, increasing out to redshift $z = 2$. These predictions may be tested both with future gravitational-wave observations and with observations of superluminous supernovae.

astro-ph.HE↗

arXiv2019

Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers

The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-populations of compact binaries representing different evolutionary scenarios may be present among sources detected by Advanced LIGO and Advanced Virgo. In addition to hierarchical modelling, unmodelled methods can aid in determining the number of sub-populations and their properties. In this paper, we apply Gaussian mixture model clustering to 1000 simulated gravitational-wave compact binary sources from a mixture of five sub-populations. Using both mass and spin as input parameters, we determine how many binary detections are needed to accurately determine the number of sub-populations and their mass and spin distributions. In the most difficult case that we consider, where two sub-populations have identical mass distributions but differ in their spin, which is poorly constrained by gravitational-wave detections, we find that ~ 400 detections are needed before we can identify the correct number of sub-populations.

astro-ph.HE↗

arXiv2019

Gravitational Wave Emission from 3D Explosion Models of Core-Collapse Supernovae with Low and Normal Explosion Energies

Understanding gravitational wave emission from core-collapse supernovae will be essential for their detection with current and future gravitational wave detectors. This requires a sample of waveforms from modern 3D supernova simulations reaching well into the explosion phase, where gravitational wave emission is expected to peak. However, recent waveforms from 3D simulations with multi-group neutrino transport do not reach far into the explosion phase, and some are still obtained from non-exploding models. We therefore calculate waveforms up to 0.9\,s after bounce using the neutrino hydrodynamics code \textsc{CoCoNuT-FMT}. We consider two models with low and normal explosion energy, namely explosions of an ultra-stripped progenitor with an initial helium star mass of $3.5\,M_{\odot}$, and of an $18\,M_{\odot}$ single star. Both models show gravitational wave emission from the excitation of surface g-modes in the proto-neutron star with frequencies between $\mathord{\sim}800\,\mathrm{Hz}$ and 1000\,Hz at peak emission. The peak amplitudes are about $6\, \mathrm{cm}$ and $10\, \mathrm{cm}$, respectively, which is somewhat higher than in most recent 3D models of the pre-explosion or early explosion phase. Using a Bayesian analysis, we determine the maximum detection distances for our models in simulated Advanced LIGO, Advanced Virgo, and Einstein Telescope design sensitivity noise. The more energetic $18 M_\odot$ explosion will be detectable to about $17.5 \,\mathrm{kpc}$ by the LIGO/Virgo network and to about $180\, \mathrm{kpc}$ with the Einstein Telescope.

astro-ph.HE↗

arXiv2019

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.

astro-ph.IM↗

arXiv2019

Reconstructing Gravitational Wave Core-Collapse Supernova Signals with Dynamic Time Warping

Core-collapse supernovae (CCSNe) are a potential source for ground-based gravitational wave detectors, as their predicted emission peaks in the detectors' frequency band. Typical searches for gravitational wave bursts reconstruct signals using wavelets. However, as CCSN signals contain multiple complex features in the time-frequency domain, these techniques often struggle to reconstruct the entire signal. An alternative method developed in recent years involves applying principal component analysis (PCA) to a set of simulated CCSN models. This technique enables model selection between astrophysical CCSN models as well as waveform reconstruction. However, PCA faces its own difficulties, such as being unable to reconstruct signals longer than the simulations; many CCSN simulations are stopped before the emission peaks due to insufficient computational resources. In this study, we show how combining PCA with dynamic time warping (DTW) improves the reconstruction of CCSN gravitational wave signals in Gaussian noise characteristic of Advanced LIGO at design sensitivity. For the waveforms used in this analysis, we find that the number of PCs needed to represent 90% of the data is reduced from nine to four by applying DTW, and that the match between the original and reconstructed waveforms improves for signal-to-noise ratios in the range [0,50].

astro-ph.IM↗

arXiv2018

Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy

Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources' astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, Bilby. This python code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely-available data from LIGO/Virgo, and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal model including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, how to implement new likelihood functions, and how to add new detectors. Bilby has additional functionality to do population studies using hierarchical Bayesian modelling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers.

astro-ph.IM↗

arXiv2018

Parameter Estimation and Model Selection of Gravitational Wave Signals Contaminated by Transient Detector Noise Glitches

The number of astrophysical sources detected by Advanced LIGO and Virgo is expected to increase as the detectors approach their design sensitivity. Gravitational wave detectors are also sensitive to transient noise sources created by the environment and the detector, known as `glitches'. As the rate of astrophysical sources increases, the probability that a signal will occur at the same time as a glitch also increases. This has occurred previously in the gravitational wave binary neutron star detection GW170817. In the case of GW170817, the glitch in the Livingston detector was easy to identify, and much shorter than the total duration of the signal, making it possible for the glitch to be removed. In this paper, we examine the effect of glitches on the measurement of signal parameters and Bayes factors used for model selection for much more difficult cases, where it may not be possible to determine that the glitch is present or to remove it. We include binary black holes similar to current detections, sine Gaussian bursts, and core-collapse supernovae. We find that the worst effects occur when the glitch is coincident with the signal maximum, and the signal to noise ratio (SNR) of the glitch is larger than the signal SNR. We have shown that for accurate parameter estimation of future gravitational wave signals it will be essential to develop further methods to either remove or reduce the effect of the glitches.

astro-ph.IM↗

arXiv2017

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.

astro-ph.HE↗

arXiv2016

Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data

The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced detectors are reaching sensitivity levels that allow for multiple detections of astrophysical gravitational-wave sources it is crucial to achieve a fast and accurate characterization of non-astrophysical transient noise shortly after it occurs in the detectors. Previously we presented three methods for the classification of transient noise sources. They are Principal Component Analysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB) and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study we carry out the first performance tests of these algorithms on gravitational-wave data from the Advanced LIGO detectors. We use the data taken between the 3rd of June 2015 and the 14th of June 2015 during the 7th engineering run (ER7), and outline the improvements made to increase the performance and lower the latency of the algorithms on real data. This work provides an important test for understanding the performance of these methods on real, non stationary data in preparation for the second advanced gravitational-wave detector observation run, planned for later this year. We show that all methods can classify transients in non stationary data with a high level of accuracy and show the benefits of using multiple classifiers.

astro-ph.IM↗

arXiv2016

Inferring the core-collapse supernova explosion mechanism with gravitational waves

A detection of a core-collapse supernova (CCSN) gravitational-wave (GW) signal with an Advanced LIGO and Virgo detector network may allow us to measure astrophysical parameters of the dying massive star. GWs are emitted from deep inside the core and, as such, they are direct probes of the CCSN explosion mechanism. In this study we show how we can determine the CCSN explosion mechanism from a GW supernova detection using a combination of principal component analysis and Bayesian model selection. We use simulations of GW signals from CCSN exploding via neutrino-driven convection and rapidly-rotating core collapse. Previous studies have shown that the explosion mechanism can be determined using one LIGO detector and simulated Gaussian noise. As real GW detector noise is both non-stationary and non-Gaussian we use real detector noise from a network of detectors with a sensitivity altered to match the advanced detectors design sensitivity. For the first time we carry out a careful selection of the number of principal components to enhance our model selection capabilities. We show that with an advanced detector network we can determine if the CCSN explosion mechanism is neutrino-driven convection for sources in our Galaxy and rapidly-rotating core collapse for sources out to the Large Magellanic Cloud.

astro-ph.HE↗

arXiv2015

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.

astro-ph.IM↗