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We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed “continuous commanding,” that achieves 10 s latency response time on orbit to unscheduled target-of-opportunity requests received on the ground. We show that this will allow Swift to respond to premerger (early-warning) gravitational-wave (GW) detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at or before merger time. This will dramatically increase the GW/gamma-ray burst (GRB) codetection 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 early-warning times, a complete model of the Swift attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning can double the rate of a prompt GRB detection with arcminute localization, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas ≫1000 deg2. While 140 s is beyond current GW detector sensitivities, 30–70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyberinfrastructure and motivate a focus on its reduction. Continuous commanding has been integrated as 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 (FRB), slewing 81° 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, thereby setting the earliest and deepest such constraints on high-energy activity from nonrepeating FRBs. The Swift Team invites the community to consider and propose novel scientific applications of ultra-low-latency UV, X-ray, and gamma-ray observations.

Plantar taping has been used in clinical settings as a short-term conservative treatment for plantar heel pain and related pathologies. The rise of at-home taping methods may offer patients more independence, but effectiveness has not been established. The purpose of this study was to evaluate the effects of plantar taping on foot mechanics during gait. We hypothesized that material compliance would drive mechanical effectiveness, with longitudinally inelastic tape reducing medial longitudinal arch (MLA) motion and anterior/posterior (A/P) plantar tissue spreading forces, and laterally inelastic tape reducing medial/lateral (M/L) tissue spreading. We also hypothesized that these effects would be influenced by foot structure. Fifteen healthy participants were tested in a randomized cross-over study design. Barefoot (BF) plus four taping methods were evaluated, including two inelastic tapes (Low-Dye, LD, and FasciaDerm, FD) along with longitudinally elastic kinesiology tape (KT) and a novel laterally elastic kinesiology tape (FAST, FS). Participants’ arch height and flexibility were measured followed by instrumented gait analysis with a multi-segment foot model. Ankle eversion and MLA drop/rise were calculated from rearfoot and forefoot reference frames, while plantar tissue spreading was calculated from shear stresses. ANOVAs with Holm pairwise tests evaluated tape effects while correlations connected arch structure and taping effectiveness (α = 0.05). The three longitudinally inelastic tapes (LD, FD, FS) reduced MLA drop by 11–15% compared with KT and BF. In late stance, these tapes also inhibited MLA rise (LD by 29%, FD and FS by 10–15%). FS and FD reduced A/P spreading forces, while FD reduced M/L spreading forces compared with all other conditions. Arch height had a moderately strong correlation (r = -0.67) with the difference in MLA drop between BF and FS. At-home plantar taping can affect the mechanical function of the foot, but tape elasticity direction matters. Longitudinally elastic kinesiology tape has little effect on mechanics, while inelastic tapes control MLA drop but also restrict MLA rise in late stance. Lateral elasticity does not limit tissue spreading and may increase comfort without sacrificing MLA control. At-home taping has the potential to broaden conservative treatment of plantar heel pain, flat foot deformity, and related pathologies, but additional studies are needed to connect mechanics with symptom relief.

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 from a nonnegligible 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., demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO–Virgo–KAGRA's (LVK) 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 LVK'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 an LVK 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 can be correctly identified.

Gravitational waves, ripples in space-time that cause the physical distance between points to change in time, were first predicted by Albert Einstein in the early twentieth century. One of the most promising sources of gravitational waves was thought to be the merger of binary neutron stars, which were also believed to generate extremely energetic bursts of light known as gamma-ray bursts and an optical transient known as a kilonova. Large interferometers capable of measuring distances of 10−18 m were built to detect gravitational waves. My dissertation research has been focused on detecting gravitational waves rapidly for the purpose of searching for electromagnetic or other signals from merging neutron star binaries. I contributed to the first-ever detection of gravitational waves in 2015, and every detection since. My work to rapidly identify new gravitational wave detections led to the first joint multi-messenger detection made in 30 years on August 17, 2017, when gravitational waves from a binary neutron star merger were observed in coincidence with a short gamma-ray burst, which led astronomers around the world to point their instruments at the region of the sky where the signal was estimated to have came from in time to observe counterparts across several electromagnetic bands. In this dissertation I will discuss each of these historical detections and the analysis I co-developed and helped perform.

The LIGO-Virgo-KAGRA collaboration uses multiple independent search pipelines to detect gravitational waves, often resulting in multiple triggers (g-events) for a single astrophysical source. These triggers are grouped into superevents, raising a critical question for multimessenger astronomy: which g-event provides the most accurate sky localization for electromagnetic follow-up? Currently, the g-event with the highest signal-to-noise ratio (SNR) is selected, under the assumption that it should provide the best estimators of the source's parameters, including its location on the sky. Analysis of simulated signals reveals systematic deviations from this expectation. In particular, a false-alarm rate (FAR)-based selector performs slightly better than the SNR-based method, but introduces pipeline biases. We present a neural network-based selector trained on simulated signals to identify the g-event with the minimum searched area -- a metric quantifying localization accuracy. The network uses information (detector SNRs, FAR, and chirp mass) from all of the triggers associated with each astrophysical source and is designed to be pipeline-agnostic. Our results show that the neural network outperforms both traditional selectors, achieving a mean searched area ~2% smaller than the SNR-based selector. Unlike FAR-based selection, the neural network preserves the underlying distribution of pipeline contributions, avoiding systematic biases toward specific pipelines. The network can be trained in approximately one minute on a few thousand events and performs event selection instantaneously, making it suitable for low-latency applications. These results demonstrate that machine learning can enhance multimessenger astronomy capabilities while maintaining fairness across detection pipelines. We recommend implementing this approach for future observing runs.

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.

Low-latency gravitational-wave search pipelines provide early-warning alerts for multimessenger astrophysical transients. Current pipelines whiten the data stream using acausal, linear-phase filters, which require a look-ahead buffer that introduces several seconds of algorithmic latency. Eliminating this latency requires causal, minimum-phase whitening filters using only past data. However, operating causal filters under non-stationary noise is non-trivial: the drifting power spectral density must be tracked without degrading the matched-filter signal-to-noise ratio (SNR), filter updates must preserve the minimum-phase condition, and the altered phase response must be compensated to maintain sky-localization accuracy. In Paper I we introduced a gauge theoretic signal processing framework and showed that the minimum-phase connection on the manifold of power spectra provides a geometrically exact update rule for causal filters. Here we validate that framework numerically and operationally, demonstrating that parallel transport along this connection strictly preserves the minimum-phase property while exactly conserving the matched-filter SNR. We numerically certify the flatness of this connection, showing that the optimal filter is a path-independent state function of the instantaneous noise. Through an injection campaign on O3 data with 15,347 binary black hole signals across the LIGO-Virgo network, we confirm that this architecture preserves the detection sensitivity and inter-detector timing and phase accuracy of the linear-phase baseline. Implementing the framework in the production sgnl pipeline reduces whitening latency by 1.0 s (33%) at a 4-second noise estimation cadence, confirmed in controlled tests and on live O3 replay data at production scale. Stride reduction experiments show that up to 91% of baseline trigger latency can be eliminated with sub-second pipeline cadence.

The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use Python package based on the jax framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.

We present SGNL, a scalable, low-latency gravitational-wave search pipeline. It reimplements the core matched-filtering principles of the GstLAL pipeline within a modernized framework. The Streaming Graph Navigator library, a lightweight Python streaming framework, replaces GstLAL's GStreamer infrastructure, simplifying pipeline construction and enabling flexible, modular graph design. The filtering core is reimplemented in PyTorch, allowing SGNL to leverage GPU acceleration for improved computational scalability. We describe the pipeline architecture and introduce a novel implementation of the Low-Latency Online Inspiral Detection algorithm in which components are pre-synchronized to reduce latency. Results from a 40-day Mock Data Challenge show that SGNL's event recovery and sensitivity are consistent with GstLAL's within statistical and systematic uncertainties. Notably, SGNL achieves a median latency of 5.4 seconds, a 42\\% reduction compared to GstLAL's 9.3 seconds.

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, qcr manifold. The template bank contains \\(1.8 10^6\\) sets of template parameters covering plausible neutron star and black hole systems up to a total mass of \\(400\\) \\(M_\\) with component masses between \\(1\\)-\\(200\\) \\(M_\\) 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, qcr 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 qcr 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.