arXiv subjects

Ik Siong Heng

Publications and source records attributed to Ik Siong Heng.

At least 37 records · Page 2Linked to original sources

arXiv2022

Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts

Coherent WaveBurst is a generic, multidetector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on selected attributes in the multidimensional event attribute space to distinguish between noisy events from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a postprocessing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic gravitational wave burst signal morphologies as well as the astrophysical source such as core-collapse supernova models due to the application of our Gaussian mixture model approach to coherent WaveBurst triggers. The Gaussian mixture model method recovers the gravitational wave signals from massive compact binary coalescences identified by coherent WaveBurst targeted for binary black holes in GWTC-2, with better significance than the all-sky coherent WaveBurst search. No additional significant gravitational wave bursts are observed.

gr-qc↗

arXiv2021

Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. For binary neutron star and neutron star black hole systems prompt counterpart electromagnetic (EM) signatures are expected on timescales of 1 second -- 1 minute and the current fastest method for alerting EM follow-up observers, can provide estimates in $\mathcal{O}(1)$ minute, on a limited range of key source parameters. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates. The training procedure need only be performed once for a given prior parameter space and the resulting trained machine can then generate samples describing the posterior distribution $\sim 6$ orders of magnitude faster than existing techniques.

astro-ph.IM↗

arXiv2021

A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a $\sim$90$\times$ speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future.

astro-ph.HE↗

arXiv2021

Exploring the sky localization and early warning capabilities of third generation gravitational wave detectors in three-detector network configurations

This work characterises the sky localization and early warning performance of networks of third generation gravitational wave detectors, consisting of different combinations of detectors with either the Einstein Telescope or Cosmic Explorer configuration in sites in North America, Europe and Australia. Using a Fisher matrix method which includes the effect of earth rotation, we estimate the sky localization uncertainty for $1.4\text{M}\odot$-$1.4\text{M}\odot$ binary neutron star mergers at distances $40\text{Mpc}$, $200\text{Mpc}$, $400\text{Mpc}$, $800\text{Mpc}$, $1600\text{Mpc}$, and an assumed astrophysical population up to redshift of 2 to characterize its performance for binary neutron star observations. We find that, for binary neutron star mergers at $200\text{Mpc}$ and a network consisting of the Einstein Telescope, Cosmic Explorer and an extra Einstein Telescope-like detector in Australia(2ET1CE), the upper limit of the size of the 90% credible region for the best localized 90% signals is $0.25\text{deg}^2$. For the simulated astrophysical distribution, this upper limit is $91.79\text{deg}^2$. If the Einstein Telescope-like detector in Australia is replaced with a Cosmic Explorer-like detector(1ET2CE), for $200\text{Mpc}$ case, the upper limit is $0.18\text{deg}^2$, while for astrophysical distribution, it is $56.77\text{deg}^2$. We note that the 1ET2CE network can detect 7.2% more of the simulated astrophysical population than the 2ET1CE network. In terms of early warning performance, we find that a network of 2ET1CE and 1ET2CE networks can both provide early warnings of the order of 1 hour prior to merger with sky localization uncertainties of 30 square degrees or less. Our study concludes that the 1ET2CE network is a good compromise between binary neutron stars detection rate, sky localization and early warning capabilities.

astro-ph.IM↗

arXiv2021

Inclination estimates from off-axis GRB afterglow modelling

For gravitational wave (GW) detected neutron star mergers, one of the leading candidates for electromagnetic (EM) counterparts is the afterglow from an ultra-relativistic jet. Where this afterglow is observed, it will likely be viewed off-axis, such as the afterglow following GW170817/GRB 170817A. The temporal behaviour of an off-axis observed GRB afterglow can be used to reveal the lateral jet structure, and statistical model fits can put constraints on the various model free-parameters. Amongst these parameters is the inclination of the system to the line of sight. Along with the GW detection, the afterglow modelling provides the best constraint on the inclination to the line-of-sight and can improve the estimates of cosmological parameters e.g. the Hubble constant, from GW-EM events. However, modelling of the afterglow depends on the assumed jet structure and, often overlooked, the effects of lateral spreading. Here we show how the inclusion of lateral spreading in the afterglow models can affect the estimated inclination of GW-EM events.

astro-ph.HE↗

arXiv2021

Rare Events Detected with a Bulk Acoustic Wave High Frequency Gravitational Wave Antenna

This work describes the operation of a High Frequency Gravitational Wave detector based on a cryogenic Bulk Acoustic Wave (BAW) cavity and reports observation of rare events during 153 days of operation over two seperate experimental runs (Run 1 and Run 2). In both Run 1 and Run 2 two modes were simultaneously monitored. Across both runs, the 3rd overtone of the fast shear mode (3B) operating at 5.506 MHz was monitored, while in Run 1 the second mode was chosen to be the 5th OT of the slow shear mode (5C) operating at 8.392 MHz. However, in Run 2 the second mode was selected to be closer in frequency to the first mode, and chosen to be the 3rd overtone of the slow shear mode (3C) operating at 4.993 MHz. Two strong events were observed as transients responding to energy deposition within acoustic modes of the cavity. The first event occurred during Run 1 on the 12/05/2019 (UTC), and was observed in the 5.506 MHz mode, while the second mode at 8.392 MHz observed no event. During Run 2, a second event occurred on the 27/11/2019(UTC) and was observed by both modes. Timing of the events were checked against available environmental observations as well as data from other detectors. Various possibilities explaining the origins of the events are discussed.

gr-qc↗

arXiv2020

Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models

Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.

gr-qc↗

arXiv2020

Optic disc and fovea localisation in ultra-widefield scanning laser ophthalmoscope images captured in multiple modalities

We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%

eess.IV↗

arXiv2020

Comparing Short Gamma-Ray Burst Jet Structure Models

A structured gamma-ray burst jet could explain the dimness of the prompt emission observed from GRB$\,170817$A but the exact form of this structure is still ambiguous. However, with the promise of future joint gravitational wave and gamma-ray burst observations, we shall be able to examine populations of binary neutron star mergers rather than a case-by-case basis. We present an analysis that considers gravitational wave triggered binary neutron star events both with and without short gamma-ray burst 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 less 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 $ \sim9.1(4.4)^{\circ} $, and the outer beaming angle to be within $\sim19.9(8.5)^{\circ}$. Similarly we show the width of a Gaussian jet structure to be constrained to $\sim2.8(1.6)^{\circ}$.

astro-ph.HE↗

arXiv2020

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.

gr-qc↗

arXiv2019

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%.

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

Black holes, gravitational waves and fundamental physics: a roadmap

The grand challenges of contemporary fundamental physics---dark matter, dark energy, vacuum energy, inflation and early universe cosmology, singularities and the hierarchy problem---all involve gravity as a key component. And of all gravitational phenomena, black holes stand out in their elegant simplicity, while harbouring some of the most remarkable predictions of General Relativity: event horizons, singularities and ergoregions. The hitherto invisible landscape of the gravitational Universe is being unveiled before our eyes: the historical direct detection of gravitational waves by the LIGO-Virgo collaboration marks the dawn of a new era of scientific exploration. Gravitational-wave astronomy will allow us to test models of black hole formation, growth and evolution, as well as models of gravitational-wave generation and propagation. It will provide evidence for event horizons and ergoregions, test the theory of General Relativity itself, and may reveal the existence of new fundamental fields. The synthesis of these results has the potential to radically reshape our understanding of the cosmos and of the laws of Nature. The purpose of this work is to present a concise, yet comprehensive overview of the state of the art in the relevant fields of research, summarize important open problems, and lay out a roadmap for future progress.

gr-qc↗

arXiv2018

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.

astro-ph.HE↗

arXiv2018

GLADE: A Galaxy Catalogue for Multi-Messenger Searches in the Advanced Gravitational-Wave Detector Era

We introduce a value-added full-sky catalogue of galaxies, named as Galaxy List for the Advanced Detector Era, or GLADE. The purpose of this catalogue is to (i) help identifications of host candidates for gravitational-wave events, (ii) support target selections for electromagnetic follow-up observations of gravitational-wave candidates, (iii) provide input data on the matter distribution of the local universe for astrophysical or cosmological simulations, and (iv) help identifications of host candidates for poorly localised electromagnetic transients, such as gamma-ray bursts observed with the InterPlanetary Network. Both being potential hosts of astrophysical sources of gravitational waves, GLADE includes inactive and active galaxies as well. GLADE was constructed by cross-matching and combining data from five separate (but not independent) astronomical catalogues: GWGC, 2MPZ, 2MASS XSC, HyperLEDA and SDSS-DR12Q. GLADE is complete up to $d_L = 37^{+3}_{-4}$ Mpc in terms of the cumulative B-band luminosity of galaxies within luminosity distance $d_L$, and contains all of the brightest galaxies giving half of the total B-band luminosity up to $d_L = 91$ Mpc. As B-band luminosity is expected to be a tracer of binary neutron star mergers (currently the prime targets of joint GW+EM detections), our completeness measures can be used as estimations of completeness for containing all binary neutron star merger hosts in the local universe.

astro-ph.HE↗

arXiv2018

Binary Neutron Star Mergers and Third Generation Detectors: Localization and Early Warning

For third generation gravitational wave detectors, such as the Einstein Telescope, gravitational wave signals from binary neutron stars can last up to a few days before the neutron stars merge. To estimate the measurement uncertainties of key signal parameters, we develop a Fisher matrix approach which accounts for effects on such long duration signals of the time-dependent detector response and the earths rotation. We use this approach to characterize the sky localization uncertainty for gravitational waves from binary neutron stars at 40, 200, 400, 800 and 1600Mpc, for the Einstein Telescope and Cosmic Explorer individually and operating as a network. We find that the Einstein Telescope alone can localize the majority of detectable binary neutron stars at a distance of $\leq200$Mpc to within $100\text{deg}^2$ with 90% confidence. A network consisting of the Einstein Telescope and Cosmic Explorer can enhance the sky localization performance significantly - with the 90% credible region of $\mathcal{O}(1) \text{deg}^2$ for most sources at $\leq200$Mpc and $\leq100\text{deg}^2$ for most sources at $\leq1600$Mpc. We also investigate the prospects for third generation detectors identifying the presence of a signal prior to merger. To do this, we require a signal to have a network signal-to-noise ratio of $\geq12$ and $\geq5.5$ for at least two interferometers, and to have a 90% credible region for the sky localization that is no larger than $100 \text{deg}^2$. We find that the Einstein Telescope can send out such "early-warning" detection alerts 1 - 20 hours before merger for 100% of detectable binary neutron stars at 40Mpc and for $\sim58\%$ of sources at 200Mpc. For sources at a distance of 400Mpc, a network of the Einstein telescope and Cosmic Explorer can produce detection alerts up to $\sim 3$ hours prior to merger for 98% of detectable binary neutron stars.

astro-ph.HE↗

arXiv2018

Strategies for the Follow-up of Gravitational Wave Transients with the Cherenkov Telescope Array

The observation of the electromagnetic counterpart of gravitational-wave (GW) transient GW170817 demonstrated the potential in extracting astrophysical information from multimessenger discoveries. The forthcoming deployment of the first telescopes of the Cherenkov Telescope Array (CTA) observatory will coincide with Advanced LIGO/Virgo's next observing run, O3, enabling the monitoring of gamma-ray emission at E > 20 GeV, and thus particle acceleration, from GW sources. CTA will not be greatly limited by the precision of GW localization as it will be be capable of rapidly covering the GW error region with sufficient sensitivity. We examine the current status of GW searches and their follow-up effort, as well as the status of CTA, in order to identify some of the general strategies that will enhance CTA's contribution to multimessenger discoveries.

astro-ph.HE↗

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↗