arXiv subjects

Ryan Magee

Publications and source records attributed to Ryan Magee.

At least 19 recordsLinked to original sources

arXiv2026

Method to get Better Sky Maps in a GstLAL Low-Latency Analysis

Modeled gravitational wave searches correlate the strain data with a bank of gravitational wave template waveforms to make detections of gravitational wave candidates, and these results are processed by downstream tools to calculate the likely sky location and distance of the source of the candidates. This is crucial for multi-messenger efforts, since it informs astronomers where to point their telescopes to facilitate electromagnetic follow-up of the gravitational wave candidates. We present a novel method to improve the low-latency results of the GstLAL gravitational wave search pipeline, and thus improving sky location estimates of low-latency candidates. This method involves ingesting the GstLAL low-latency results, and performing a small targeted hierarchical search to recover the candidates with more accurate parameters, in a medium-latency timescale (few seconds to five minutes). To test our method, we perform a GstLAL low-latency analysis on forty days of data from the third observing run of LIGO, Virgo, and KAGRA, and show that our method improves the GstLAL results by 5.38% and the subsequent sky location results by 16.75% on average. In addition to this increase in precision, we also show that these results are more accurate as compared to the GstLAL results. This method has been adopted by GstLAL for the fourth observing run.

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arXiv2026

GstLAL O4 Online Results Paper

Gravitational-wave observations of merging binary neutron stars and black holes are now routinely made by detectors in the Advanced LIGO-Virgo-KAGRA network. Neutron star binary systems may also produce detectable electromagnetic and particle emission over times scales ranging from seconds to years. Real-time gravitational-wave searches play a central role in enabling time-critical electromagnetic and/or neutrino follow-up observations. During the fourth observing run (O4) of the Advanced LIGO-Virgo-KAGRA network, multiple real-time searches operated continuously to identify candidate gravitational-wave events and publicly disseminate information about these discoveries. Here, the performance and results of the GstLAL real-time analysis are reported. The analysis is designed to identify candidates with low latency, high detection efficiency, and sustained operational uptime over long observing periods. Across O4, it produced initial candidate uploads with a median latency of 15.8 s while maintaining an effective uptime of 98% during the first two parts of the observing run. During the run, the analysis contributed to 250 candidates classified as astrophysically plausible, provided the first upload for 222 of these, and was the sole contributor for 75. Among Gravitational-Wave Transient Catalog events with a false-alarm rate below one per year, 88% were identified as significant in low latency and promoted for expert vetting and public dissemination. The low-latency astrophysical classifications agreed with the final catalog classifications for 93% of the events considered.

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arXiv2025

GW231109_235456: A Sub-threshold Binary Neutron Star Merger in the LIGO-Virgo-KAGRA O4a Observing Run?

We present a subthreshold search for gravitational-wave inspirals from binary neutron stars using data from the first part of the fourth observing run of the LIGO-Virgo-KAGRA Collaboration. To enhance sensitivity to this targeted population, we incorporate a redshift-corrected population model informed by radio observations of Galactic double neutron star systems. The search identifies a significant trigger with a false-alarm rate of one per fifty years and a network signal-to-noise ratio of 9.7. This trigger was first reported in low-latency processing as S231109ci and subsequently listed in the GWTC-4.0 catalog as GW231109_235456, a subthreshold candidate. Accounting for a trials factor of five arising from four previous searches in GWTC-4.0 and this new search, the false-alarm rate of the candidate is approximately one per ten years. If the event is of astrophysical origin, the inferred source properties indicate component masses of 1.40 to 2.24 solar masses for the primary and 0.97 to 1.49 solar masses for the secondary, yielding a total mass of 2.95 solar masses with an uncertainty of plus 0.38 and minus 0.07 solar masses. The event is localized to a region of 450 square degrees enclosing ninety percent probability at a luminosity distance of 165 megaparsecs with an uncertainty of plus 70 and minus 69 megaparsecs. Assuming the signal arises from a binary neutron star merger, we estimate the local merger rate to lie between 53 and 342 per cubic gigaparsec per year.

astro-ph.HE↗

arXiv2025

New Methods for Offline GstLAL Analyses

In this work, we present new methods implemented in the GstLAL offline gravitational wave search. These include a technique to reuse the matched filtering data products from a GstLAL online analysis, which hugely reduces the time and computational resources required to obtain offline results; a technique to combine these results with a separate search for heavier black hole mergers, enabling detections from a larger set of gravitational wave sources; changes to the likelihood ratio which increases the sensitivity of the analysis; and two separate changes to the background estimation, allowing more precise significance estimation of gravitational wave candidates. Some of these methods increase the sensitivity of the analysis, whereas others correct previous mis-estimations of sensitivity by eliminating false positives. These methods have been adopted for GstLAL's offline results during the fourth observing run of LIGO, Virgo, and KAGRA (O4). To test these new methods, we perform an offline analysis over one chunk of O3 data, lasting from May 12 19:36:42 UTC 2019 to May 21 14:45:08 UTC 2019, and compare it with previous GstLAL results over the same period of time. We show that cumulatively these methods afford around a 50% - 100% increase in sensitivity in the highest mass space, while simultaneously increasing the reliability of results, and making them more reusable and computationally cheaper.

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arXiv2024

Template bank for sub solar mass compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Matched-filtering searches for gravitational-wave signals from compact binary mergers employ template banks which are a collection of modeled waveforms described by unique intrinsic parameters. We present two banks designed for low-latency and archive sub-solar mass (SSM) searches in data from the fourth observing run of LIGO-Virgo-KAGRA, and demonstrate the efficacy of the banks via simulated signals. Further, we introduce a set of modifications to the geometric, manifold algorithm that allow the method to work for exceedingly low component masses necessary for SSM bank production. The archive search bank contains a total of $3,452,006$ templates, and covers a mass parameter space of $0.2$ to $10\ M_\odot$ in the larger component and $0.2$ to $1.0\ M_\odot$ in the smaller component, the spin parameter space of $-0.9$ to $0.9$ for masses above $0.5$ $M_\odot$ and $-0.1$ to $0.1$ for masses below $0.5$ $M_\odot$, and the mass ratio parameter space of $1$ to $10$. The PSD used was from a week of the first half of the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA, and the low frequency cutoff was set to $45$ Hz with a maximum waveform duration of $128$ seconds. The bank simulations performed using SBank have shown that the banks presented in this paper have sufficient efficacy for use in their respective searches.

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arXiv2024

Scalable matched-filtering pipeline for gravitational-wave searches of compact binary mergers

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core.

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arXiv2024

Mitigating the impact of noise transients in gravitational-wave searches using reduced basis timeseries and convolutional neural networks

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or glitches) that can contaminate the data. Although detection pipelines utilize a variety of noise mitigation techniques, glitches can occasionally bypass these checks and produce false positives. One class of mitigation techniques is the signal consistency check, which aims to quantify how similar the observed data is to the expected signal. In this work, we describe a new signal consistency check that utilizes a set of bases that spans the gravitational-wave signal space and convolutional neural networks (CNN) to probabilistically identify glitches. We recast the basis response as a grayscale image, and train a CNN to distinguish between gravitational-waves and glitches with similar morphologies. We find that the CNN accurately classifies $\gtrsim 99\%$ of the responses it is shown. We compare these results to a toy detection pipeline, finding that the two methods produce similar false positive rates, but that the CNN has a significantly higher true positive rate. We modify our toy model detection pipeline and demonstrate that including information from the network increases the toy pipeline's true positive rate by $4-7\%$ while decreasing the false positive rate to a data-limited bound of $\lesssim 0.1\%$.

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arXiv2024

Swiftly chasing gravitational waves across the sky in real-time

We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed `continuous commanding,' achieving 10 seconds latency response time on-orbit to unscheduled Target of Opportunity requests. This allows Swift to respond to early warning gravitational-wave detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at merger time. This will dramatically increase the GW/GRB co-detection rate, and enable prompt arcminute localization of a neutron star merger. We simulate the full Swift response to a GW early warning alert, including input sky maps produced at different warning times, a complete model of Swift's attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning doubles the rate of prompt GRB detections with arcminute position, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas >> 1000 deg$^2$. While 140 s is beyond current gravitational wave detector sensitivities, 30-70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyber-infrastructure, and motivate focus on its reduction. Continuous commanding is now a general capability of Swift, significantly increasing its versatility in response to the growing demands of time-domain astrophysics. We demonstrate this potential on an externally triggered Fast Radio Burst, slewing 81 degrees across the sky, and collecting X-ray and UV photons from the source position < 150 s after the trigger was received from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), thereby setting the earliest and deepest such constraints on high energy activity from non-repeating FRBs. The Swift Team invites proposals for novel scientific applications of ultra-low latency UV, X-ray, and gamma-ray observations.

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arXiv2024

A neural network-based gravitational wave interpolant with applications to low-latency analyses

Matched-filter based gravitational-wave search pipelines identify candidate events within seconds of their arrival on Earth, offering a chance to guide electromagnetic follow-up and observe multi-messenger events. Understanding the detectors' response to an astrophysical transient across the searched signal manifold is paramount to inferring the parameters of the progenitor and deciding which candidates warrant telescope time. We describe a framework that uses artificial neural networks to interpolate gravitational waves and, equivalently, the signal-to noise ratio (SNR) across sufficiently local patches of the signal manifold. Our machine-learning based model generates a single waveform in 6 milliseconds on a CPU and 0.4 milliseconds on a GPU. When using a GPU to generate batches of waveforms simultaneously, we find that we can produce $10^4$ waveforms in $\lesssim 1$ ms. This is achieved while remaining faithful, on average, to 1 part in $10^4$ (1 part in $10^5$) for binary black hole (binary neutron star) waveforms. The model we present is designed to directly utilize intermediate detection pipeline outputs in the hopes of facilitating a better real-time understanding of gravitational-wave candidates.

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arXiv2024

Searching for asymmetric and heavily precessing Binary Black Holes in the gravitational wave data from the LIGO and Virgo third Observing Run

Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily misaligned spins in LIGO and Virgo data taken during the third observing run. Our target systems show strong imprints of precession whereas current searches have non-optimal sensitivity in detecting them. After measuring the sensitivity improvement brought by our search over standard spin-aligned searches, we report the detection of 30 gravitational wave events already discovered in the latest version of the Gravitational Wave Transient Catalog. However, we do not find any additional significant gravitational wave candidates. Our results allow us to place an upper limit of $R_{90\%} = 0.28^{+0.33}_{-0.04}\;\; \mathrm{Gpc^{-3}yr^{-1}}$ on the merger rate of a hypothetical subpopulation of asymmetric, heavily precessing signals, not identified by other searches. Since our upper limit is consistent with the latest rate estimates from the LIGO-Virgo-KAGRA collaboration, our findings rule out the existence of a yet-to-be-discovered population of precessing binaries.

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arXiv2024

Searching for gravitational-wave signals from precessing black hole binaries with the GstLAL pipeline

Precession in Binary Black Holes (BBH) is caused by the failure of the Black Hole spins to be aligned and its study can open up new perspectives in gravitational waves (GW) astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly non-trivial task, as standard matched filtering pipelines for GW searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio $q\lesssim 6$, a volume increase of up to 100\% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the $\xi^2$ signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections.

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arXiv2023

The impact of selection biases on tests of general relativity with gravitational-wave inspirals

Tests of general relativity with gravitational wave observations from merging compact binaries continue to confirm Einstein's theory of gravity with increasing precision. However, these tests have so far only been applied to signals that were first confidently detected by matched-filter searches assuming general relativity templates. This raises the question of selection biases: what is the largest deviation from general relativity that current searches can detect, and are current constraints on such deviations necessarily narrow because they are based on signals that were detected by templated searches in the first place? In this paper, we estimate the impact of selection effects for tests of the inspiral phase evolution of compact binary signals with a simplified version of the GstLAL search pipeline. We find that selection biases affect the search for very large values of the deviation parameters, much larger than the constraints implied by the detected signals. Therefore, combined population constraints from confidently detected events are mostly unaffected by selection biases, with the largest effect being a broadening at the $\sim10$ % level for the $-1$PN term. These findings suggest that current population constraints on the inspiral phase are robust without factoring in selection biases. Our study does not rule out a disjoint, undetectable binary population with large deviations from general relativity, or stronger selection effects in other tests or search procedures.

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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.

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arXiv2023

Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.

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arXiv2023

When to Point Your Telescopes: Gravitational Wave Trigger Classification for Real-Time Multi-Messenger Followup Observations

We develop a robust and self-consistent framework to extract and classify gravitational wave candidates from noisy data, for the purpose of assisting in real-time multi-messenger follow-ups during LIGO-Virgo-KAGRA's fourth observing run~(O4). Our formalism implements several improvements to the low latency calculation of the probability of astrophysical origin~(\PASTRO{}), so as to correctly account for various factors such as the sensitivity change between observing runs, and the deviation of the recovered template waveform from the true gravitational wave signal that can strongly bias said calculation. We demonstrate the high accuracy with which our new formalism recovers and classifies gravitational wave triggers, by analyzing replay data from previous observing runs injected with simulated sources of different categories. We show that these improvements enable the correct identification of the majority of simulated sources, many of which would have otherwise been misclassified. We carry out the aforementioned analysis by implementing our formalism through the \GSTLAL{} search pipeline even though it can be used in conjunction with potentially any matched filtering pipeline. Armed with robust and self-consistent \PASTRO{} values, the \GSTLAL{} pipeline can be expected to provide accurate source classification information for assisting in multi-messenger follow-up observations to gravitational wave alerts sent out during O4.

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arXiv2023

Improved ranking statistics of the GstLAL inspiral search for compact binary coalescences

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to optimize the detection algorithm of low-latency search pipelines, increasing their sensitivities to gravitational waves from compact binary coalescences. In this work, we discuss several new features developed for ranking statistics of GstLAL-based inspiral pipeline, which mainly consist of: the signal contamination removal, the bank-$\xi^2$ incorporation, the upgraded $\rho-\xi^2$ signal model and the integration of KAGRA. An injection study demonstrates that these new features improve the pipeline's sensitivity by approximately 15% to 20%, paving the way to further multi-messenger observations during the upcoming observing run.

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arXiv2023

Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA's fourth observing run

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation.

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arXiv2022

Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Matched-filtering gravitational wave search pipelines identify gravitational wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational wave detector data and gravitational wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational wave waveforms are often stored in "template banks", and the construction of a densely populated template bank is essential for some gravitational wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, {\fontfamily{qcr}\selectfont manifold}. The template bank contains $1.8 \times 10^6$ sets of template parameters covering plausible neutron star and black hole systems up to a total mass of $400$ $M_\odot$ with component masses between $1$-$200$ $M_\odot$ and mass ratios between $1$ and $20$ under the assumption that each component object's angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, {\fontfamily{qcr}\selectfont manifold}, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The {\fontfamily{qcr}\selectfont GstLAL} search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly $5$ times as compared to previously reported SVD sorting schemes.

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