arXiv subjects

Daniel Williams

Publications and source records attributed to Daniel Williams.

At least 19 recordsLinked to original sources

arXiv2026

Expanding the scope of dark siren cosmology: Inferring the population properties of gravitational wave-hosting galaxies

The field of gravitational wave "dark siren" cosmology is facing a number of challenges surrounding the way in which galaxy catalogue data is incorporated into the analysis. The three biggest challenges are adequately compensating for galaxy catalogue incompleteness, robust treatment of galaxy redshift and source properties, and the as-yet unknown host galaxy weighting model. We present an updated methodology for inferring cosmological parameters using dark sirens and incomplete galaxy catalogues. By switching to a sampled redshift prior, rather than one which is pre-computed on a grid, we can jointly infer parameters which describe the population of gravitational-wave hosting galaxies, alongside the gravitational wave population and cosmological model. Using the cosmological inference software gwcosmo, we reproduce a subset of GWTC-5.0 cosmology results, then extend the analysis to jointly infer the host galaxy weighting model - the first measurement of its kind to do so - leading to an updated measurement of the Hubble constant with $H_0 = {71.9}_{-7.5}^{+9.1}$ km s$^{-1}$ Mpc$^{-1}$ (median and 68% symmetric credible interval). This innovative new method opens the door to solving several of the biggest challenges currently facing the field of dark siren cosmology.

astro-ph.CO↗

arXiv2026

A catalogue of insights from the fourth LIGO-Virgo-KAGRA observing run

Over the last decade, gravitational-wave science has developed from an era where a single detection was a Nobel-winning achievement to an (almost) everyday occurrence. The LIGO-Virgo-KAGRA (LVK) Collaboration's flagship scientific results are now their catalogues of detections, which include a comprehensive set of analysis results characterising the astrophysical properties of sources, and their implications for fundamental physics and cosmology. We review discoveries from the fourth observing run (O4) of the LVK network. The most recent catalogues each include $\sim100$ new detections. Among these new detections are remarkable single discoveries: the loudest signals detected so far, and sources unlike those seen before. However, significant advances in the field are coming from population-level analyses, leveraging the increasing sample size to infer constraints on the astrophysical distribution of merging compact binaries, potential deviations to general relativity, and the expansion rate of the Universe. Analyses of the latest data have revealed evidence for multiple subpopulations of sources. These observations provide a unique probe into the evolution of massive stars and the formation of coalescing binaries. Our understanding of gravitational-wave signals and their sources will continue to deepen with more observations. The final catalogue from O4}is due for release in December 2026, and future observing runs with even greater detector sensitivity are planned. These will bring even larger catalogues, and additional loud signals, continuing to advance gravitational-wave science.

astro-ph.HE↗

arXiv2026

Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Masking

Real-time, deep learning-based vocal denoising has seen significant progress over the past few years, demonstrating the capability of artificial intelligence in preserving the naturalness of the voice while increasing the signal-to-noise ratio (SNR). However, many deep learning approaches have high amounts of latency and require long frames of context, making them difficult to configure for live applications. To address these challenges, we propose a sigmoid-driven ideal ratio mask trained with a spectral loss to encourage an increased SNR and maximized perceptual quality of the voice. The proposed model uses a band-grouped encoder-decoder architecture with frequency attention and achieves a total latency of less than 10,ms, with PESQ-WB improvements of 0.21 on stationary noise and 0.12 on nonstationary noise.

cs.SD↗

arXiv2025

Classport: Designing Runtime Dependency Introspection for Java

Runtime introspection of dependencies, i.e., the ability to observe which dependencies are currently used during program execution, is fundamental for Software Supply Chain security. Yet, Java has no support for it. We solve this problem with Classport, a blueprint and system that embeds dependency information into Java class files, enabling the retrieval of dependency information at runtime. We evaluate Classport on six real-world projects, demonstrating the feasibility in identifying dependencies at runtime.

cs.SE↗

arXiv2025

LensingFlow: An Automated Workflow for Gravitational Wave Lensing Analyses

In this work, we present LensingFlow. This is an implementation of an automated workflow to search for evidence of gravitational lensing in a large series of gravitational wave events. This workflow conducts searches for evidence in all generally considered lensing regimes. The implementation of this workflow is built atop the Asimov automation framework and CBCFlow metadata management software and the resulting product therefore encompasses both the automated running and status checking of jobs in the workflow as well as the automated production and storage of relevant metadata from these jobs to allow for later reproduction. This workflow encompasses a number of existing lensing pipelines and has been designed to accommodate any additional future pipelines to provide both a current and future basis on which to conduct large scale lensing analyses of gravitational wave signal catalogues. The workflow also implements a prioritisation management system for jobs submitted to the schedulers in common usage in computing clusters ensuring both the completion of the workflow across the entire catalogue of events as well as the priority completion of the most significant candidates. As a first proof-of-concept demonstration, we deploy LensingFlow on a mock data challenge comprising 10 signals in which signatures of each lensing regime are represented. LensingFlow successfully ran and identified the candidates from this data through its automated checks of results from consituent analyses.

gr-qc↗

arXiv2025

Querying Databases with Function Calling

The capabilities of Large Language Models (LLMs) are rapidly accelerating largely thanks to their integration with external tools. Querying databases is among the most effective of these integrations, enabling LLMs to access private or continually updating data. While Function Calling is the most common method for interfacing external tools to LLMs, its application to database querying as a tool has been underexplored. We propose a tool definition for database querying that unifies accessing data with search queries, filters, or a combination both, as well as transforming results with aggregation and groupby operators. To evaluate its effectiveness, we conduct a study with 8 LLMs spanning 5 model families. We present a novel pipeline adapting the Gorilla LLM framework to create synthetic database schemas and queries. We primarily evaluate the models with the Exact Match of predicted and ground truth query APIs. Among the models tested, Claude 3.5 Sonnet achieves the highest performance with an Exact Match score of 74.3%, followed by GPT-4o mini at 73.7%, and GPT-4o at 71.8%. We further breakdown these results per API component utilized and across synthetic use cases. We find that LLMs are highly effective at utilizing operators on boolean properties, but struggle with text property filters. Across use cases we find robust results with the higher performing models such as GPT-4o, but significant performance variance across use cases from lower performing models. We additionally conduct ablation studies exploring the impact of parallel tool calling, adding a rationale as an argument of the tool call, using a separate tool per database collection, and tool calling with structured outputs. Our findings demonstrate the effectiveness of enabling LLMs to query databases with Function Calling. We have open-sourced our experimental code and results at github.com/weaviate/gorilla.

cs.DB↗

arXiv2024

Beyond GWTC-3: Analysing and verifying new gravitational-wave events from community catalogues

The public release of data from the LIGO and Virgo detectors has enabled the identification of potential gravitational wave signals by independent teams using alternative methodologies. In addition to the LIGO-Virgo-KAGRA (LVK) collaboration's GWTC-3 catalogue there have been several additional works claiming the detection of signals in the data from the first three observing runs. In this paper we present an analysis of these new signals using the same analysis workflow which was used to generate the GWTC-2.1 and GWTC-3 catalogues published by the LVK, matching the analysis configuration as closely as possible, and we provide our parameter estimation results in a format comparable to those of the GWTC-3 data release. We also include a discussion of the workflow developed for this analysis.

astro-ph.HE↗

arXiv2023

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. Combining the GW data with the optical counterpart, also known as AT2017gfo, classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonova brought about a revolution in the domain of multi-messenger astronomy, since there has been only one kilonova from a gravitational wave detected binary neutron star merger event so far, this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder trained on light curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once trained, the time scale for light curve generation is of the order of a few milliseconds, thus speeding up generating light curves by $1000$ times compared to the simulation. The mean squared error between the generated and original light curves is typically $0.015$ with a maximum of $0.08$ for each set of considered physical parameter; while having a maximum of $\approx0.6$ error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results.

astro-ph.HE↗

arXiv2022

Observational limits on the rate of radiation-driven binary black hole capture events

Dense astrophysical environments like globular clusters and galactic nuclei can host hyperbolic encounters of black holes which can lead to gravitational-wave driven capture. There are several astrophysical models which predict a fraction of binary black hole mergers to come from these radiation-driven capture scenarios. In this paper we present the sensitivity of a search towards gravitational-wave driven capture events for O3, the third observing run of LIGO and Virgo. We use capture waveforms produced by numerical relativity simulations covering four different mass ratios and at least two different values of initial angular momentum per mass ratio. We employed the most generic search for short-duration transients in O3 to evaluate the search sensitivity in this parameter space for a wide range in total mass in terms of visible spacetime volume. From the visible spacetime volume we determine for the first time the merger rate upper limit of such systems. The most stringent estimate of rate upper limits at 90\% confidence is $0.2~\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$ for an equal mass $200~M_\odot$ binary. Furthermore, in recent studies the event GW190521 has been suggested to be a capture event. With this interpretation of GW190521, we find the merger rate of similar events to be $0.47~\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$.

gr-qc↗

arXiv2022

Asimov: A framework for coordinating parameter estimation workflows

Since the first detection in 2015 of gravitational waves from compact binary coalescence, improvements to the Advanced LIGO and Advanced Virgo detectors have expanded our view into the universe for these signals. Searches of the of the latest observing run (O3) have increased the number of detected signals to 90, at a rate of approximately 1 per week. Future observing runs are expected to increase this even further. Bayesian analysis of the signals can reveal the properties of the coalescing black holes and neutron stars by comparing predicted waveforms to the observed data. The proliferating number of detected signals, the increasing number of methods that have been deployed, and the variety of waveform models create an ever-expanding number of analyses that can be considered. Asimov is a python package which is designed to simplify and standardise the process of configuring these analyses for a large number of events. It has already been used in developing analyses in three major gravitational wave catalog publications.

gr-qc↗

arXiv2022

Mimicking Mergers: Mistaking Black Hole Captures as Mergers

As the number of gravitational wave observations has increased in recent years, the variety of sources has broadened. Here we investigate whether it is possible for the current generation of detectors to distinguish between very short-lived gravitational wave signals from mergers between high-mass black holes, and the signal produced by a close encounter between two black holes which results in gravitational capture, and ultimately a merger. We compare the posterior probability distributions produced by analysing simulated signals from both types of progenitor events, both under ideal and realistic scenarios. We show that while, under ideal conditions it is possible to distinguish both progenitors, under more realistic conditions they are indistinguishable. This has important implications for the interpretation of such short signals, and we therefore advocate that these signals be the focus of additional investigation even when satisfactory results have been achieved from standard analyses.

gr-qc↗

arXiv2021

General-relativistic precession in a black-hole binary

The general-relativistic phenomenon of spin-induced orbital precession has not yet been observed in strong-field gravity. Gravitational-wave observations of binary black holes (BBHs) are prime candidates, since we expect the astrophysical binary population to contain precessing binaries. Imprints of precession have been investigated in several signals, but no definitive identification of orbital precession has been reported in any one of the 84 BBH observations to date by the Advanced LIGO and Virgo detectors. Here we report the measurement of strong-field precession in the LIGO-Virgo-Kagra (LVK) gravitational-wave signal GW200129. The binary's orbit precesses at a rate ten orders of magnitude faster than previous weak-field measurements from binary pulsars. We also find that the primary black hole is likely highly spinning. According to current binary population estimates a GW200129-like signal is extremely unlikely, and therefore presents a direct challenge to many current binary formation models.

gr-qc↗

arXiv2021

Anomalous Jet Identification via Sequence Modeling

This paper presents a novel method of searching for boosted hadronically decaying objects by treating them as anomalous elements of a contaminated dataset. A Variational Recurrent Neural Network (VRNN) is used to model jets as sequences of constituent four-vectors. After applying a pre-processing method which boosts each jet to the same reference mass and energy, the VRNN provides each jet an Anomaly Score that distinguishes between the structure of signal and background jets. The model is trained in an entirely unsupervised setting and without high level variables, making the score more robust against mass and $p_{T}$ correlations when compared to methods based primarily on jet substructure. Performance is evaluated on the jet level, as well as in an analysis context by searching for a heavy resonance with a final state of two boosted jets. The Anomaly Score shows consistent performance along a wide range of signal contamination amounts, for both two and three-pronged jet substructure hypotheses. Analysis results demonstrate that the use of Anomaly Score as a classifier enhances signal sensitivity while retaining a smoothly falling background jet mass distribution. The model's discriminatory performance resulting from an unsupervised training scenario opens up the possibility to train directly on data without a pre-defined signal hypothesis.

hep-ph↗

arXiv2021

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

hep-ph↗

arXiv2020

Evidence for hierarchical black hole mergers in the second LIGO--Virgo gravitational-wave catalog

We study the population properties of merging binary black holes in the second LIGO--Virgo Gravitational-Wave Transient Catalog assuming they were all formed dynamically in gravitationally bound clusters. Using a phenomenological population model, we infer the mass and spin distribution of first-generation black holes, while self-consistently accounting for hierarchical mergers. Considering a range of cluster masses, we see compelling evidence for hierarchical mergers in clusters with escape velocities $\gtrsim 100~\mathrm{km\,s^{-1}}$. For our most probable cluster mass, we find that the catalog contains at least one second-generation merger with $99\%$ credibility. We find that the hierarchical model is preferred over an alternative model with no hierarchical mergers (Bayes factor $\mathcal{B} > 1400$) and that GW190521 is favored to contain two second-generation black holes with odds $\mathcal{O}>700$, and GW190519, GW190602, GW190620, and GW190706 are mixed-generation binaries with $\mathcal{O} > 10$. However, our results depend strongly on the cluster escape velocity, with more modest evidence for hierarchical mergers when the escape velocity is $\lesssim 100~\mathrm{km\,s^{-1}}$. Assuming that all binary black holes are formed dynamically in globular clusters with escape velocities on the order of tens of $\mathrm{km\,s^{-1}}$, GW190519 and GW190521 are favored to include a second-generation black hole with odds $\mathcal{O}>1$. In this case, we find that $99\%$ of black holes from the inferred total population have masses that are less than $49\,M_{\odot}$, and that this constraint is robust to our choice of prior on the maximum black hole mass.

astro-ph.HE↗

arXiv2020

Enhancing Gravitational-Wave Science with Machine Learning

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.

astro-ph.HE↗

arXiv2019

Comparing Short Gamma-Ray Burst Jet Structure Models

A structured gamma-ray burst jet could explain the dimness of the prompt emission observed from GRB$\,170817$A but the exact form of this structure is still ambiguous. However, with the promise of future joint gravitational wave and gamma-ray burst observations, we shall be able to examine populations of binary neutron star mergers rather than a case-by-case basis. We present an analysis that considers gravitational wave triggered binary neutron star events both with and without short gamma-ray burst counterparts assuming that events without a counterpart were observed off-axis. This allows for Bayes factors to be calculated to compare different jet structure models. We perform model comparison between a Gaussian and power-law apparent jet structure on simulated data to demonstrate that the correct model can be distinguished with a log Bayes factor of $>5$ after less than 100 events. Constraints on the apparent structure jet model parameters are also made. After 25(100) events the angular width of the core of a power-law jet structure can be constrained within a $90\%$ credible interval of width $ \sim9.1(4.4)^{\circ} $, and the outer beaming angle to be within $\sim19.9(8.5)^{\circ}$. Similarly we show the width of a Gaussian jet structure to be constrained to $\sim2.8(1.6)^{\circ}$.

astro-ph.HE↗

arXiv2019

A Precessing Numerical Relativity Waveform Surrogate Model for Binary Black Holes: A Gaussian Process Regression Approach

Gravitational wave astrophysics relies heavily on the use of matched filtering both to detect signals in noisy data from detectors, and to perform parameter estimation on those signals. Matched filtering relies upon prior knowledge of the signals expected to be produced by a range of astrophysical systems, such as binary black holes. These waveform signals can be computed using numerical relativity techniques, where the Einstein field equations are solved numerically, and the signal is extracted from the simulation. Numerical relativity simulations are, however, computationally expensive, leading to the need for a surrogate model which can predict waveform signals in regions of the physical parameter space which have not been probed directly by simulation. We present a method for producing such a surrogate using Gaussian process regression which is trained directly on waveforms generated by numerical relativity. This model returns not just a single interpolated value for the waveform at a new point, but a full posterior probability distribution on the predicted value. This model is therefore an ideal component in a Bayesian analysis framework, through which the uncertainty in the interpolation can be taken into account when performing parameter estimation of signals.

gr-qc↗