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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 th

The taggedPBC (Ring 2025a) contains more than 1,800 sentences of pos-tagged parallel text data from over 1,500 languages, representing 133 language families and 111 isolates. While this dwarfs previously available resources, and the POS tags achieve decent accuracy, allowing for predictive crosslinguistic insights (Ring 2025b), the dataset was not initially annotated for dependencies. This paper reports on a CoNLLU-formatted version of the dataset which transfers dependency information along with POS tags to all languages in the taggedPBC. Although there are various concerns regarding the quality of the tags and the dependencies, word order information derived from this dataset regarding the position of arguments and predicates in transitive clauses correlates with expert determinations of word order in three typological databases (WALS, Grambank, Autotyp). This highlights the usefulness of corpus-based typological approaches (as per Baylor et al. 2023; Bjerva 2024) for extending comparisons of discrete linguistic categories, and suggests that important insights can be gained even from noisy data, given sufficient annotation. The dependency-annotated corpora are also made available

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 preci

Experience with experimental and computational work are important components of students' understanding of the practice of physics. Physics departments typically use specific experimental lab courses and computational lab courses to develop students' skills in these areas. However these siloed experiences do not accurately represent the nuanced interplay between the theory, computation, and experiment that occurs outside of the curricular setting when physicists are building and testing models of the physical world. To expose students to this interplay, we have integrated computational and experimental work as equal partners within two radioactivity labs that occur within our modern physics lab course - the decay of $^{137}{\rm Ba}$ and the simultaneous decay of two silver isotopes in pre-1965 quarters. We present both of these labs and potential extensions that allow students to iterate on their theoretical, computational and/or experimental models based on what they learn from their initial investigation.

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 cumula

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 degre

We describe the effort to characterize gravitational-wave searches and detector sensitivity to different types of compact binary coalescences during the LIGO-Virgo-KAGRA Collaborations' fourth observing run. We discuss the design requirements and example use cases for this data product, constructed from $> 4.33\times10^8$ injections during O4a alone. We also identify subtle effects with high confidence, like diurnal duty cycles within detectors. This paper accompanies a public data release of the curated injection set, and the appendixes give detailed examples of how to use the publicly available data.

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 Stream 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 40 days of data 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 4.7 seconds, compared to 9.0 seconds for GstLAL.

Searches for gravitational waves from compact binary coalescences employ a process called matched filtering, in which gravitational wave strain data is cross-correlated against a bank of waveform templates. Data from every observing run of the LIGO, Virgo, and KAGRA collaboration is typically analyzed in this way twice, first in a low-latency mode in which gravitational wave candidates are identified in near-real time, and later in a high-latency mode. Such high-latency analyses have traditionally been considered more sensitive, since background data from the full observing run is available for assigning significance to all candidates, as well as more robust, since they do not need to worry about keeping up with live data. In this work, we present a novel technique to use the matched filtering data products from a low-latency analysis and re-process them by assigning significances in a high-latency way, effectively removing the need to perform matched filtering a second time. To demonstrate the efficacy of our method, we analyze 38 days of LIGO and Virgo data from the third observing run (O3) using the GstLAL pipeline, and show that our method is as sensitive and reliable as a trad

Concerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring the reliability of applications relying on LLM-generated content. Current methods often demand substantial resources and rely on extensive LLMs or employ supervised learning with multidimensional features or intricate linguistic and semantic analyses difficult to reproduce and largely depend on using the same LLM that hallucinated. This paper introduces a supervised learning approach employing two simple classifiers utilizing only four numerical features derived from tokens and vocabulary probabilities obtained from other LLM evaluators, which are not necessarily the same. The method yields promising results, surpassing state-of-the-art outcomes in multiple tasks across three different benchmarks. Additionally, we provide a comprehensive examination of the strengths and weaknesses of our approach, highlighting the significance of the features utilized and the LLM employed as an evaluator. We have released our code publicly at https://github.com/Baylor-AI/HalluDetect.

Reproducibility is a fundamental requirement for validating scientific claims in computational research. Stochastic computational models are widely used in fields such as systems biology, financial modeling and environmental sciences. However, achieving reproducibility in stochastic simulations remains challenging, as each run can produce different outcomes. Existing infrastructure and software tools do not address independent reproduction of simulation results. Without independent reproducibility, results and conclusions lack credibility, as it remains unclear whether observed findings reflect model behavior or are artifacts of stochastic variation or an underpowered study. To bridge this gap, we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a data-driven method to quantify the reproducibility of stochastic simulation results. EFECT employs empirical characteristic functions to compare reported results with those independently generated by assessing distributional inequality, termed EFECT error. Additionally, we establish the EFECT convergence point, a quantitative metric for determining the required number of simulation runs to achieve an EFEC

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

While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological databases, including WALS and Grambank, suffer from inconsistencies primarily caused by their categorical format. Furthermore, typological categorisations by definition differ significantly from the continuous nature of phenomena, as found in natural language corpora. In this paper, we introduce a new seed dataset made up of continuous-valued data, rather than categorical data, that can better reflect the variability of language. While this initial dataset focuses on word-order typology, we also present the methodology used to create the dataset, which can be easily adapted to generate data for a broader set of features and languages.

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 rati

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.

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-$ξ^2$ incorporation, the upgraded $ρ-ξ^2$ 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.

Binary star systems are of particular interest to astronomers because they can be used as astrophysical laboratories to study the properties and processes of stars. Between 70% to 90% of the stars in our galaxy are part of a binary star system. Among the many types of binary systems observed, the dynamics of semi-detached and contact systems are the most interesting because they exhibit mass transfer, which changes the composition and life cycle of both stars. The time scales of the mass transfer process are extremely large which makes the process impossible to capture through physical observation. Computer simulations have proved invaluable in refining our understanding of the mass transfer processes. Here we introduce an intuitive, computationally efficient, gravity centered model that simulates the filling of the Roche lobe of an expanding star and its transfer of mass through the first Lagrangian point.

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods withi

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