arXiv subjects

Linqing Wen

Publications and source records attributed to Linqing Wen.

At least 19 recordsLinked to original sources

arXiv2025

Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run

The detection of gravitational waves from compact binary coalescences has provided significant insights into our Universe, and the discovery of new and unique gravitational wave candidates from independent searches remains an ongoing field of research. In this work, we built a hybrid search pipeline that combines matched filtering and deep learning to identify stellar-mass binary black hole candidates from detector strain data. We first present results from a targeted injection study to benchmark the sensitivity of our method and compare it with existing search pipelines. We demonstrate that our hybrid approach has comparable sensitivity for injections with a source-frame chirp mass greater than 25$\,$M$_{\odot}$, and below this threshold our sensitivity drops off for signals with a network SNR less than 15. We also observe that our search method can identify a significant population of unique candidates. Furthermore, we conduct an offline search for gravitational wave candidates in the third observing run of the LIGO-Virgo-KAGRA Collaboration (LVK), yielding 31 candidates previously reported by the LVK with a probability of astrophysical origin $p_{\rm astro}\geq0.5$. We identify two other candidates: one previously reported only in a search conducted by the Institute for Advanced Study, and one previously unreported promising new candidate with a $p_{\rm astro}$ of 0.63. This unique candidate has a high chirp mass and a high probability that the primary black hole is an intermediate-mass black hole.

gr-qc↗

arXiv2025

Cryoscope: A Cryogenic Infrared Survey Telescope in Antarctica

We present Cryoscope--a new 50 deg$^2$ field-of-view, 1.2 m aperture, $K_{dark}$ survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope is cryogenically cooled. Cryoscope also explores new detector technology to cost-effectively tile the full focal plane. Leveraging the dark Antarctic sky and minimizing telescope thermal emission, Cryoscope achieves unprecedented deep, wide, fast and red observations, matching and exceeding volumetric survey speeds from the Ultraviolet Explorer, Vera Rubin Observatory, Nancy Grace Roman Space Telescope, SPHEREx, and NEO Surveyor. By providing coverage beyond wavelengths of 2 $\mu$m, we aim to create the most comprehensive dynamic movie of the most obscured reaches of the Universe. Cryoscope will be a dedicated discovery engine for electromagnetic emission from coalescing compact binaries, Earth-like exoplanets orbiting cold stars, and multiple facets of time-domain, stellar and solar system science. In this paper, we describe the scientific drivers and technical innovations for this new discovery engine operating in the $K_{dark}$ passband, why we choose to deploy it in Antarctica, and the status of a fifth-scale prototype designed as a Pathfinder to retire technological risks prior to full-scale implementation. We plan to deploy the Cryoscope Pathfinder to Dome C in December 2026 and the full-scale telescope by 2030.

astro-ph.IM↗

arXiv2024

Binary Neutron Star Merger Search Pipeline Powered by Deep Learning

Gravitational waves are now routinely detected from compact binary mergers, with binary neutron star mergers being of note for multi-messenger astronomy as they have been observed to produce electromagnetic counterparts. Novel search pipelines for these mergers could increase the combined search sensitivity, and could improve the ability to detect real gravitational wave signals in the presence of glitches and non-stationary detector noise. Deep learning has found success in other areas of gravitational wave data analysis, but a sensitive deep learning-based search for binary neutron star mergers has proven elusive due to their long signal length. In this work, we present a deep learning pipeline for detecting binary neutron star mergers. By training a convolutional neural network to detect binary neutron star mergers in the signal-to-noise ratio time series, we concentrate signal power into a shorter and more consistent timescale than strain-based methods, while also being able to train our network to be robust against glitches. We compare our pipeline's sensitivity to the three offline detection pipelines using injections in real gravitational wave data, and find that our pipeline has a comparable sensitivity to the current pipelines below the 1 per 2 months detection threshold. Furthermore, we find that our pipeline can increase the total number of binary neutron star detections by 12% at a false alarm rate of 1 per 2 months. The pipeline is also able to successfully detect the two binary neutron star mergers detected so far by the LIGO-Virgo-KAGRA collaboration, GW170817 and GW190425, despite the loud glitch present in GW170817.

gr-qc↗

arXiv2024

Gravitational-wave matched filtering with variational quantum algorithms

In this paper, we explore the application of variational quantum algorithms designed for classical optimization to the problem of matched filtering in the detection of gravitational waves. Matched filtering for detecting gravitational wave signals requires searching through a large number of template waveforms, to find one which is highly correlated with segments of detector data. This computationally intensive task needs to be done quickly for low latency searches in order to aid with follow-up multi-messenger observations. The variational quantum algorithms we study for this task consist of quantum walk-based generalizations of the Quantum Approximate Optimization Algorithm (QAOA). We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO. These results show that the tested variational quantum algorithms are outperformed by an unstructured restricted-depth Grover search algorithm, suggesting that the latter is optimal for this computational task.

quant-ph↗

arXiv2023

Novel Deep Learning Approach to Detecting Binary Black Hole Mergers

Gravitational wave detection has opened up new avenues for exploring and understanding some of the fundamental principles of the universe. The optimal method for detecting modelled gravitational-wave events involves template-based matched filtering and performing a multi-detector coincidence search in the resulting signal-to-noise ratio time series. In recent years, advancements in machine learning and deep learning have led to a flurry of research into using these techniques to replace matched filtering searches and for efficient and robust parameter estimation of the gravitational wave sources. This paper presents a feasibility study for a novel approach to detecting binary black hole gravitational wave signals, which utilizes deep learning techniques on the signal-to-noise ratio time series produced from matched filtering. We show that a deep-learning search can efficiently detect binary black hole gravitational waves from the signal-to-noise ratio time series in simulated Gaussian noise with simulated transient glitches. Furthermore, our search method can outperform a maximum SNR-based matched filtering search on simulated data of the Hanford and Livingston LIGO detectors in the presence of glitches. We further demonstrate that our approach can improve the detection sensitivity for binary black hole mergers at lower masses, relative to a baseline sensitivity of existing search pipelines and deep learning approaches. Lastly, since we are building upon the foundations of a matched filtering search pipeline, we can extract estimates for the signal-to-noise ratio and detector frame chirp mass of a gravitational wave event with similar accuracy as existing pipelines.

gr-qc↗

arXiv2023

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger searches for BNS and NSBH mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to GWs has resumed with O4. To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a MDC in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5s) and show consistency and accuracy of released data products using the MDC. For the first time, we report the expected median latency for triggers from early warning searches (-3.1s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LVK low-latency alert infrastructure and data products using the MDC and serves as a useful reference for the interpretation of O4 detections.

astro-ph.HE↗

arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗

arXiv2022

Pre-merger sky localization of gravitational waves from binary neutron star mergers using deep learning

The simultaneous observation of gravitational waves (GW) and prompt electromagnetic counterparts from the merger of two neutron stars can help reveal the properties of extreme matter and gravity during and immediately after the final plunge. Rapid sky localization of these sources is crucial to facilitate such multi-messenger observations. Since GWs from binary neutron star (BNS) mergers can spend up to 10-15 mins in the frequency bands of the detectors at design sensitivity, early warning alerts and pre-merger sky localization can be achieved for sufficiently bright sources, as demonstrated in recent studies. In this work, we present pre-merger BNS sky localization results using CBC-SkyNet, a deep learning model capable of inferring sky location posterior distributions of GW sources at orders of magnitude faster speeds than standard Markov Chain Monte Carlo methods. We test our model's performance on a catalog of simulated injections from Sachdev et al. (2020), recovered at 0-60 secs before merger, and obtain comparable sky localization areas to the rapid localization tool BAYESTAR. These results show the feasibility of our model for rapid pre-merger sky localization and the possibility of follow-up observations for precursor emissions from BNS mergers.

astro-ph.HE↗

arXiv2022

The most probable host of CHIME FRB 190425A, associated with binary neutron star merger GW190425, and a late-time transient search

The identification and localization of Fast Radio Bursts to their host galaxies has revealed important details about the progenitors of these mysterious, millisecond-long bursts of coherent radio emission. In this work we study the most probable host galaxy of the apparently non-repeating CHIME/FRB event FRB 20190425A -- a particularly high luminosity, low dispersion measure event that was demonstrated by Moroianu et al. 2022 to be temporally and spatially coincident with the LIGO-Virgo-KAGRA binary neutron star merger GW190425, suggesting an astrophysical association (p-value 0.0052). In this paper we remain agnostic to this result, and we confirm UGC10667 as the most probable host galaxy of FRB 20190425A, demonstrating that the host galaxies of low dispersion measure, one-off CHIME FRBs can be plausibly identified. We then perform multi-wavelength observations to characterize the galaxy and search for any afterglow emission associated with the FRB and its putative GW counterpart. We find no radio or optical transient emission in our observations $2.5\,\mathrm{yr}$ post-burst. UGC10667 is a spiral galaxy at $z\sim0.03$, dominated by an old stellar population. We find no evidence of a large population of young stars, with nebular emission dominated by star formation at a rate of $1-2\,\mathrm{M_\odot\,yr^{-1}}$. While we cannot rule out a young magnetar as the origin of FRB 20190425A, our observations are consistent with an origin in a long delay-time neutron star binary merger as posited by Moroianu et al. 2022.

astro-ph.HE↗

arXiv2022

An assessment of the Association Between a Fast Radio Burst and Binary Neutron Star Merger

Fast radio bursts (FRBs) are mysterious bright millisecond-duration radio bursts at cosmological distances. While young magnetars have been put forward as the leading source candidate, recent observations suggest there may be multiple FRB progenitor classes. It has long been theorised that FRBs could be emitted from compact object mergers - cataclysmic events such as binary neutron star (BNS) mergers that may be detectable in gravitational waves (GWs) by the ground-based Laser Interferometer Gravitational Wave Observatory (LIGO)and Virgo. Here we report a potential coincidence between the only BNS merger event GW190425 out of 21 GW sources detected during the first six months of LIGO-Virgo's 3rd Science Run and a bright, non-repeating FRB event, FRB 20190425A, from a search using public GW and CHIME FRB data. The FRB is located within the GW's sky localization area, occurred 2.5 hours after the GW event, and has a dispersion measure consistent with the distance inferred from GW parameter estimation. The chance probability of a coincidence between unrelated FRB and GW events in the databases is estimated to be 0.0052 ($2.8 \sigma$). We estimate the chance of CHIME detecting such an event to range from 0.4% for a beam-centre detection to 68% if a bright burst is detectable in a far sidelobe. This potential association is consistent with the theory that the BNS merger leaves behind a supramassive, highly magnetized compact object, which collapses to form a black hole after losing angular momentum due to spindown and makes an FRB through ejecting the magnetosphere. If such a physical association is established, the equation of state of the post-merger compact object is likely stiff, with a Tolman-Oppenheimer-Volkoff non-spinning maximum mass $M_{TOV} > 2.63_{-0.23}^{+0.39} M_\odot$ for a neutron star remnant, or $M_{TOV} > 2.31_{-0.08}^{+0.24} M_\odot$ for a quark star remnant.

astro-ph.HE↗

arXiv2022

Rapid localization of gravitational wave sources from compact binary coalescences using deep learning

The mergers of neutron star-neutron star and neutron star-black hole binaries are the most promising gravitational wave events with electromagnetic counterparts. The rapid detection, localization and simultaneous multi-messenger follow-up of these sources is of primary importance in the upcoming science runs of the LIGO-Virgo-KAGRA Collaboration. While prompt electromagnetic counterparts during binary mergers can last less than two seconds, the time scales of existing localization methods that use Bayesian techniques, varies from seconds to days. In this paper, we propose the first deep learning-based approach for rapid and accurate sky localization of all types of binary coalescences, including neutron star-neutron star and neutron star-black hole binaries for the first time. Specifically, we train and test a normalizing flow model on matched-filtering output from gravitational wave searches. Our model produces sky direction posteriors in milliseconds using a single P100 GPU, which is three to six orders of magnitude faster than Bayesian techniques.

gr-qc↗

arXiv2022

Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we present two neural network models trained to rapidly estimate the posterior distributions of the chirp mass and mass ratio of a detected binary black hole system from the GW strain data of LIGO Hanford and Livingston Observatories. Using these parameters the component masses can be predicted, which has implications for the prediction of the likelihood that a merger contains a neutron star. The results are compared to the 'gold standard' of parameter estimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC), LALInference. Our models predict posterior distributions consistent with that from LALInference while using orders of magnitude less processing time once the models are trained. The median predictions are within the 90% credible intervals of LALInference for all predicted parameters when tested on real binary black hole events detected during the LVC's first and second observing runs. We argue that deep learning has strong potential for low-latency high-accuracy parameter estimation suitable for real-time GW search pipelines.

astro-ph.IM↗

arXiv2021

Early Warnings of Binary Neutron Star Coalescence using the SPIIR Search

Gravitational waves from binary neutron star mergers can be used as alerts to enable prompt follow-up observations. In particular, capturing prompt electromagnetic and astroparticle emissions from the moment of a binary merger presents unique constraints on the time scale and sky localization for online gravitational wave detection. Here we present the expected performance of the SPIIR online detection pipeline that is designed for this purpose in the upcoming international LIGO-Virgo's 4th Science Run (O4). Using simulated Gaussian data for the two LIGO observatories with expected O4 sensitivity, we demonstrate that there is a non-negligible opportunity to deliver pre-merger warnings at least 10 s before the final plunge. These alerts are expected to be issued at a nominal rate of one binary neutron star coalescence per year and localized within a median searched area of 300 $deg^2$. We envision such a detection to be extremely useful for follow-up observatories with a large field of view such as the Murchison Widefield Array radio facility at Western Australia.

gr-qc↗

arXiv2021

Semianalytical Approach for Sky Localization of Gravitational Waves

Rapid sky localization of gravitational wave sources is crucial to enable prompt electromagnetic follow-ups. In this article, we present a novel semianalytical approach for sky localization of gravitational waves from compact binary coalescences. We use the Bayesian framework with an analytical approximation to the prior distributions for a given astrophysical model. We derive a semianalytical solution to the posterior distribution of source directions. This method only requires one-fold numerical integral that marginalizes over the merger time, compared to the five-fold numerical integration otherwise needed in the Bayesian localization method. The performance of the method is demonstrated using a set of binary neutron stars (BNS) injections on Gaussian noise using LIGO-Virgo's design and O2 sensitivity. We find the median of 90% confidence area in O2 sensitivity to be $\mathcal{O}(10^2) ~\mathrm{deg}^2$, comparable to that of the existing LIGO-Virgo online localization method Bayestar and parameter estimation toolkit LALInference. In the end, we apply this method to localize the BNS event GW170817 and find the 50% (90%) confidence region of 11 $\mathrm{deg}^2$ (50 $\mathrm{deg}^2$). The detected optical counterpart of GW170817 resides within our 50% confidence area.

gr-qc↗

arXiv2021

Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.

gr-qc↗

arXiv2021

First demonstration of early warning gravitational wave alerts

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 non-retracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like $\gamma$-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during - and in the seconds following - merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multi-messenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early-warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.

astro-ph.HE↗

arXiv2020

The SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences

This paper presents the SPIIR pipeline used for public alerts during the third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering and this process is further accelerated with graphics processing units (GPUs). It is the first online pipeline to select candidates from multiple detectors using a coherent statistic based on the maximum network likelihood ratio statistic principle. Here we simplify the derivation of this statistic using the singular-value-decomposition (SVD) technique and show that single-detector signal-to-noise ratios from matched filtering can be directly used to construct the statistic for each sky direction. Coherent searches are in general more computationally challenging than coincidence searches due to extra search over sky direction parameters. The search over sky directions follows an embarrassing parallelization paradigm and has been accelerated using GPUs. The detection performance is reported using a segment of public data from LIGO-Virgo's second observation run. We demonstrate that the median latency of the SPIIR pipeline is less than 9 seconds, and present an achievable roadmap to reduce the latency to less than 5 seconds. During the O3 online run, SPIIR registered triggers associated with 38 of the 56 non-retracted public alerts. The extreme low-latency nature makes it a competitive choice for joint time-domain observations, and offers the tantalizing possibility of making public alerts prior to the merger phase of binary coalescence systems involving at least one neutron star.

gr-qc↗

arXiv2019

Ground-Based Gravitational-Wave Astronomy in Australia: 2019 White Paper

The past four years have seen a scientific revolution through the birth of a new field: gravitational-wave astronomy. The first detection of gravitational waves---recognised by the 2017 Nobel Prize in Physics---provided unprecedented tests of general relativity while unveiling a previously unknown class of massive black holes, thirty times more massive than the Sun. The subsequent detection of gravitational waves from a merging binary neutron star confirmed the hypothesised connection between binary neutron stars and short gamma-ray bursts while providing an independent measurement of the expansion of the Universe. The discovery enabled precision measurement of the speed of gravity while shedding light on the origin of heavy elements. At the time of writing, the Laser Interferometer Gravitational-wave Observatory (LIGO) and its European partner, Virgo, have published the detection of eleven gravitational-wave events. New, not-yet-published detections are announced on a nearly weekly basis. This fast-growing catalogue of gravitational-wave transients is expected to yield insights into a number of topics, from the equation of state of matter at supra-nuclear densities to the fate of massive stars. The science potential of 3G observatories is enormous, enabling measurements of gravitational waves from the edge of the Universe and precise determination of the neutron star equation of state. Australia is well-positioned to help develop the required technology. The Mid-term Review for the Decadal plan for Australian astronomy 2016-2025 should consider investment in a scoping study for an Australian Gravitational-Wave Pathfinder that develops and validates core technologies required for the global 3G detector network.

astro-ph.IM↗