arXiv subjects

Man Leong Chan

Publications and source records attributed to Man Leong Chan.

At least 19 recordsLinked to original sources

arXiv2026

GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run

The frequent presence of non-Gaussian transient noise, or glitches, in gravitational-wave detector data can affect gravitational-wave searches, parameter estimation, and downstream analyses. To identify and mitigate transient noise near gravitational-wave candidates in a timely manner, the LIGO-Virgo-KAGRA Collaboration employs the Data Quality Report. In the fourth observing run, GSpyNetTree-O4 was deployed within this framework as a tool for glitch classification and event validation. We describe GSpyNetTree-O4 and the main developments relative to its predecessor, GSpyNetTree. The most important update was a new architecture that allowed the simultaneous identification of glitches and gravitational-wave signals when both were present in the same input. We also expanded and augmented the training set with examples in which simulated gravitational-wave signals overlapped with real glitches, and applied $60\,\mathrm{Hz}$ calibration corrections to better match the data expected during the fourth observing run. On test data, the low-mass, high-mass, and extremely high-mass classifiers identified $97.9\%$, $97.7\%$, and $95.4\%$ of glitches, respectively. Among samples without a glitch, including gravitational-wave-only and No Glitch samples, the classifiers correctly reported no data-quality issues in $97.1\%$, $96.6\%$, and $96.0\%$ of cases, respectively. We further assessed the robustness of GSpyNetTree-O4 on unseen glitch morphologies, a small set of Virgo glitches from the fourth observing run, and different choices of the $Q$-value used to construct the time-frequency inputs. GSpyNetTree-O4 was successfully deployed as a Data Quality Report tool and increased automation in gravitational-wave event validation workflows.

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arXiv2026

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

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arXiv2026

Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap

The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained with new observations. While NSs have been observed with masses up to $\sim2~M_{\odot}$, there is a dearth of electromagnetic observations of compact objects in the $\sim2-5~M_{\odot}$ range, known as the lower mass gap. Recent observations of gravitational-wave (GW) signals from binary mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration indicate that this gap is likely not empty. Rapidly distinguishing whether a candidate GW event has components in this purported mass gap can indicate the likelihood of a detectable electromagnetic counterpart, and thus inform decisions for follow-up observations. In this work we train a neural network model, GWSkyNet-MassGap, that simultaneously predicts the probability that a candidate merger has a component in the lower mass gap ($P_{\mathrm{MassGap}}$) and the probability that it involves a NS ($P_{\mathrm{NS}}$). We find that the model is able to infer information about the source chirp mass to predict $P_{\mathrm{MassGap}}$ and $P_{\mathrm{NS}}$, leading to correct predictions for high-mass mergers with $\mathcal{M}_c\gtrsim15~M_{\odot}$, but less accurate predictions for lower-mass systems which require knowledge of the binary mass ratio to break the mass degeneracy. For candidate events in the first part of LVK's fourth observing run (O4a), the model has a mean prediction error of 9% for $P_{\mathrm{MassGap}}$ and 6% for $P_{\mathrm{NS}}$. The model could be further developed to rapidly predict the source chirp mass for candidate events in future observing runs.

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arXiv2025

GSpyNetTreeS: a machine learning solution for glitch localization in time and frequency

Data from ground-based gravitational wave detectors are often contaminated by non-Gaussian instrumental artifacts or detector noise transients. Unbiased source property estimation relies on the ability to correctly identify and characterize these artifacts and remove them if necessary. To this end, the LIGO-Virgo-KAGRA Collaboration has implemented candidate vetting for all significant candidates to identify the presence of artifacts and assess the need for mitigation. The current candidate vetting process requires human experts to identify the frequency ranges and the time windows associated with any data quality issues present. Differences in judgment between human experts may cause inconsistency, making results difficult to reproduce across gravitational wave events. We present GSpyNetTreeS, an extension to GSpyNetTree based on the You Only Look Once algorithm, for the automatic detection, classification, and time-frequency localization of detector noise transients. As a proof of concept, we tested GSpyNetTreeS's performance on the data collected by the LIGO detectors during the third observing run for gravitational waves as well as common detector glitch classes included in GSpyNetTree: Blip, Low frequency blip, Low frequency line and Scratchy. We also demonstrated that GSpyNetTreeS is capable of accurately identifying common glitch classes and capturing the frequency and time information associated with detected detector noise transients, establishing its potential as an automatic event validation tool for LIGO-Virgo-KAGRA's observing runs.

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arXiv2025

Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information

Low-latency gravitational-wave alerts provide the greater multi-messenger community with information about the candidate events detected by the International Gravitational-Wave Network (IGWN). Prompt release of data products such as the sky localization, false alarm rate (FAR), and $p_\mathrm{astro}$ values allow astronomers to make informed decisions on which candidate gravitational-wave events merit target of opportunity (ToO) follow-up. However, false alarms, often referred to as "glitches", where a gravitational-wave candidate, or trigger, is the result of terrestrial noise, are an inherent part of gravitational-wave searches. In addition, with the presence of multiple gravitational-wave searches, different searches may have varying assessments of the significance of a given trigger. As a complement to quantities such as $p_\mathrm{astro}$, we provide a Machine Learning (ML) based approach to determining whether candidate events are astrophysical or terrestrial in nature, specifically a classifier that utilizes information provided by multiple low-latency search pipelines in its feature space. This classifier has a performance an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.96 and accuracy of 0.90 on the Mock Data Challenge training set and an AUC of 0.93 and accuracy of 0.86 on events from the Advanced LIGO (aLIGO)'s and Advanced Virgo (AdVirgo)'s third observing run (O3).

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arXiv2025

GWSkyNet-Multi II: an updated machine learning model for rapid classification of gravitational-wave events

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging; the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a machine learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values that are more interpretable. For significant event alerts issued during O4a and O4b, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions.

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arXiv2025

Searching for gravitational waves from stellar-mass binary black holes early inspiral

The early inspiral from stellar-mass binary black holes (sBBHs) can emit milli-Hertz gravitational wave signals, making them detectable sources for space-borne gravitational wave missions like TianQin. However, the traditional matched filtering technique poses a significant challenge for analyzing this kind of signal, as it requires an impractically high number of templates ranging from $10^{31}$ to $10^{40}$. We propose a search strategy that involves two main parts: initially, we reduce the dimensionality of the simulated signals using incremental principal component analysis (IPCA). Subsequently, we train the convolutional neural networks (CNNs) based on the compressed TianQin data obtained from IPCA, aiming to develop both a detection model and a point parameter estimation model. The compression efficiency for the trained IPCA model achieves a cumulative variance ratio of 95.6% when applied to $10^6$ simulated signals. To evaluate the performance of CNN we generate the receiver operating characteristic curve for the detection model which is applied to the test data with varying signal-to-noise ratios. At a false alarm probability of 5%, the corresponding true alarm probability for signals with a signal-to-noise ratio of 50 is 86.5%. Subsequently, we introduce the point estimation model to evaluate the value of the chirp mass of corresponding sBBH signals with an error. For signals with a signal-to-noise ratio of 50, the trained point estimation CNN model can estimate the chirp mass of most test events, with a standard deviation error of 2.49 $M_{\odot}$and a relative error precision of 0.13.

astro-ph.SR↗

arXiv2024

GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified.

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arXiv2024

An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the Third Observing Run of Advanced LIGO and Advanced Virgo

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possible gravitational-wave emissions. For neutrino-driven explosions, the distance at which we reach 50% detection efficiency is up to 8.9 kpc, while more energetic magnetorotationally-driven explosions are detectable at larger distances. The distance reaches for selected models of the black hole formation, and quantum chromodynamics phase transition are also provided. We then constrain the core-collapse supernova engine across a wide frequency range from 50 Hz to 2 kHz. The upper limits on gravitational-wave energy and luminosity emission are at low frequencies down to $10^{-4}\,M_\odot c^2$ and $6 \times 10^{-4}\,M_\odot c^2$/s, respectively. The upper limits on the proto-neutron star ellipticity are down to 3 at high frequencies. Finally, by combining the results obtained with the data from the first and second observing runs of LIGO and Virgo, we improve the constraints of the parameter spaces of the extreme emission models. Specifically, the proto-neutron star ellipticities for the long-lasting bar mode model are down to 1 for long emission (1 s) at high frequency.

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arXiv2024

Detection, sky localization and early warning for binary neutron star mergers by detectors located in China of different configurations in third generation detector network

This work shows the results of an evaluation of the impact that a detector located in China, with a noise budget comparable to that of a proposed high-frequency detector with a 20 km arm length, an Einstein Telescope (ET) or a Cosmic Explorer (CE), could have on the network of ET-CE in terms of detection rate, localization, and providing early warning alert for simulated binary neutron star (BNS)s. The results indicate that a three-detector network including a Chinese detector could identify at least 4.4% more BNS mergers than an ET-CE network alone. The localization uncertainty could be reduced by a factor of more than 5 on average compared to the ET-CE network. With a three-detector network involving a Chinese detector, up to 89% of BNS mergers could be located within 10 square degrees of the sky 10 minutes prior to the merger. The assessment suggests that the potential for early warning signals is highest when the Chinese detector is similar to ET, whereas the sources are detected with the highest signal-to-noise ratio and localized to the smallest regions when the detector is more akin to CE. Interestingly, the C20N network (comprising ET+CE+C20) can achieve comparable localization performance as the ET network while outperforming the ETCN network (featuring the ET+CE+ an ET-like detector in China) in terms of detection capabilities, especially at large distances, indicating that adding a 20 km kilohertz detector in China to ET-CE network would make significant contributions at least as adding an ET-like detector in China to multi-messenger astronomy for almost all BNS observations.

astro-ph.CO↗

arXiv2023

Explaining the GWSkyNet-Multi machine learning classifier predictions for gravitational-wave events

GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts to produce prediction scores indicating whether an event is a merger of two black holes, a merger involving a neutron star, or a non-astrophysical glitch. This facilitates time sensitive decisions about whether to perform electromagnetic follow-up of candidate events during LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how the model is leveraging the limited information available to make its predictions. As a deep learning neural network, the inner workings of the model can be difficult to interpret, impacting our trust in its validity and robustness. We tackle this issue by systematically perturbing the model and its inputs to explain what underlying features and correlations it has learned for distinguishing the sources. We show that the localization area of the 2D sky maps and the computed coherence versus incoherence Bayes factors are used as strong predictors for distinguishing between real events and glitches. The estimated distance to the source is further used to discriminate between binary black hole mergers and mergers involving neutron stars. We leverage these findings to show that events misclassified by GWSkyNet-Multi in LVK's third observing run have distinct sky area, coherence factor, and distance values that influence the predictions and explain these misclassifications. The results help identify the model's limitations and inform potential avenues for further optimization.

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arXiv2023

Science with the Einstein Telescope: a comparison of different designs

The Einstein Telescope (ET), the European project for a third-generation gravitational-wave detector, has a reference configuration based on a triangular shape consisting of three nested detectors with 10 km arms, where in each arm there is a `xylophone' configuration made of an interferometer tuned toward high frequencies, and an interferometer tuned toward low frequencies and working at cryogenic temperature. Here, we examine the scientific perspectives under possible variations of this reference design. We perform a detailed evaluation of the science case for a single triangular geometry observatory, and we compare it with the results obtained for a network of two L-shaped detectors (either parallel or misaligned) located in Europe, considering different choices of arm-length for both the triangle and the 2L geometries. We also study how the science output changes in the absence of the low-frequency instrument, both for the triangle and the 2L configurations. We examine a broad class of simple `metrics' that quantify the science output, related to compact binary coalescences, multi-messenger astronomy and stochastic backgrounds, and we then examine the impact of different detector designs on a more specific set of scientific objectives.

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arXiv2022

Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks

Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of-principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability towards different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs towards more realistic EMRI data analysis.

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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.

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arXiv2020

Estimate of the Detectability of the Circular Polarisation Signature of Supernova Gravitational Waves Using the Stokes Parameters

The circular polarisation of gravitational waves from core collapse supernovae has been proposed as a probe to investigate the rotation and physical features inside the core of the supernovae. However, it is still unclear as to how detectable the circular polarisation of gravitational waves will be. We developed an algorithm referred to as the Stokes Circular Polarisation algorithm for the computation of the Stokes parameters that works with the burst search pipeline coherent WaveBurst. Employing the waveform SFHx and the algorithm, we estimate the detectability of the circular polarisation signatures (V mode of the Stokes parameters) for sources across the sky at three different distances 2, 5, and 10 kpc, for a network of gravitational wave detectors consisted of advanced LIGO, advanced VIRGO and KAGRA. Using the Bayes factor, we found that for 2 kpc and 5 kpc, the majority of the sources (99.9% and 58.2% respectively) will have their V mode detectable, while for 10 kpc, no significant V mode is detectable. In addition, the significance of the V mode signature are consistent with the recoverability of the two polarisations of gravitational waves with respect to the network.

astro-ph.HE↗

arXiv2020

Following up the afterglow: strategy for X-ray observation triggered by gravitational wave events

The multi-messenger observation of compact binary coalescence promises great scientific treasure. However, a synthetic observation from both gravitational wave and electromagnetic channels remains challenging. Relying on the day-to-week long macronova emission, GW170817 remains the only event with successful electromagnetic followup. In this manuscript, we explore the possibility of using the early stage X-ray afterglow to search for the electromagnetic counterpart of gravitational wave events. Two algorithms, the sequential observation and the local optimization are considered and applied to three simulated events. We consider the proposed Einstein probe as a candidate X-ray telescope. Benefiting from the large field of view and high sensitivity, we find that the sequential observation algorithm not only is easy to implement, but also promises a good chance of actual detection.

astro-ph.HE↗

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↗

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↗