arXiv subjects

Gregory Ashton

Publications and source records attributed to Gregory Ashton.

At least 19 recordsLinked to original sources

arXiv2026

Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction

In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.

gr-qc↗

arXiv2026

A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects

Bayesian inference is commonly employed in the analysis of gravitational-wave signals not only to estimate the source parameters, but also for model selection. The latter provides insight into the physics of the source and has the potential to inform the direction for future model development. Although model comparison is usually performed by analyzing the data separately with different models and comparing the obtained Bayesian evidences, an alternative approach consists in sampling directly over the model itself. Here, we present t-roo, a reversible jump Markov chain Monte Carlo sampler capable of performing transdimensional inference on gravitational-wave signals from compact binary coalescences. Employing t-roo, a single analysis provides simultaneously the model odds ratio and the parameter posteriors for the favored models, hence yielding a potentially substantial computational advantage, particularly when comparing many models or analyzing highly informative data. t-roo is built on the sampler eryn and is specifically designed to compare models describing different kinds of sources, i.e., binary black hole, binary neutron star, or neutron star-black hole systems, as well as multiple models for the same source class. We validate the sampler on a set of injections, finding agreement with the results obtained with the nested sampler dynesty. We then use t-roo to analyze the real events GW190425 and GW230529, for which the system's parameters alone do not provide conclusive evidence of the presence of a neutron star component. t-roo can be adapted to any model-comparison scenario, thus providing a valuable tool in particular for next-generation detectors, where analyzing data separately with competing models becomes computationally even more demanding.

gr-qc↗

arXiv2026

Combining gravitational wave search pipelines to find subthreshold signals in GWTC-5.0

The detection of transient gravitational wave signals relies on independent search algorithms that analyse detector data and assign significance measures to candidate events. However, varying performance complicates their interpretation. We use supervised machine learning combined with conformal prediction, a framework to quantify uncertainties, to merge multi-pipeline information into well-calibrated confidence scores. We demonstrate that this approach is robust across different classifier architectures and remains stable when trained on different simulated datasets. When applied to events across the GWTC catalogue up to and including the second part of the fourth observing run, the framework identifies several subthreshold candidates with elevated confidence, including the binary neutron star candidate GW200311_103121. We examine the reliability of these up-rankings, finding evidence that high-confidence predictions correspond to signal-like events. This framework enables simplified systematic candidate assessment for gravitational wave catalogues and real-time alerts by providing a single, well-calibrated confidence measure per candidate.

gr-qc↗

arXiv2026

Measuring the rate of glitches in interferometric gravitational wave detectors with a hierarchical Bayesian model

Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. However, terrestrial non-Gaussian noise artefacts, commonly known as glitches, reduce the sensitivity to signals and can overlap signals, producing biased astrophysical inferences. We introduce a hierarchical Bayesian model to measure the glitch rate, which improves upon existing trigger-counting methods in its capacity to measure the rate down into the low signal-to-noise regime without contamination from the Gaussian noise background, provided the population is accurately modelled. The framework accommodates any glitch model, and measures the rate with respect to the model chosen: here we use the antiglitch model, so the rate inferred is that of short-duration glitches rather than of all glitches. The methodology builds on standard hierarchical inference, but includes several novel features: hierarchical inference with quantile compression (HIQC), a generic approximation for the recycled hyperlikelihood, and a time-domain rate estimated by fitting basis functions. We validate the methodology using simulated data with injected glitches and then apply it to data from the fourth LIGO-Virgo-KAGRA observing run, demonstrating time-resolved inferences of the glitch rate over a 24 h period. The inferred glitch rate is consistent with estimates from trigger counts, but requires no arbitrary threshold and provides a more fine-grained view of the temporal behaviour. Finally, we demonstrate how our individual-detector rate estimates can be transformed into a coincident glitch probability and utilise this to provide evidence that the retracted gravitational-wave candidate GW230630_070659 is likely a pair of coincident glitches.

gr-qc↗

arXiv2026

Case studies with GPBilby of glitch-contaminated transient gravitational waves

In their fourth observing run, the LIGO--Virgo--KAGRA gravitational-wave observatories have found hundreds of new signals, but many are contaminated by non-Gaussian transient noise artefacts known as glitches. Left unaddressed, glitches can bias parameter inference and lead to misleading astrophysical conclusions. We present a series of case studies using GPBilby, a parameter estimation tool that employs a time-domain likelihood jointly modelling the astrophysical signal with a physical waveform and non-Gaussian noise with a Gaussian process. We first show that when the detector noise is Gaussian, GPBilby produces results consistent with those obtained with the standard Gaussian-noise likelihood, and then consider events affected by non-Gaussian features. For GW231123, the highest-mass binary black hole candidate observed to date, analyses using IMRPhenomXPHM reveal coherent residual structure that leads to measurable shifts in inferred source parameters. In contrast, analyses employing NRSur7dq4 show no significant excess residual power and remain consistent across likelihood choices. This demonstrates that waveform systematics and flexible noise modelling are intrinsically coupled, as the Gaussian process terms can partially absorb coherent waveform mismatches. For GW191109, we find that evidence for spin misalignment remains robust despite glitches in both LIGO detectors. For GW230630_070659, excluded from GWTC-4.0 owing to poor data quality, we find the data to be consistent with a BBH waveform model, with no additional residual power identified by the Gaussian process component. Overall, these results highlight how GPBilby can be used to perform glitch-robust inference and as a tool to understand waveform modelling systematics.

gr-qc↗

arXiv2026

Identifying Host Galaxies of Binary Black Hole Mergers with Next-Generation Gravitational Wave Detector Networks

Identifying the host galaxy of a binary black hole (BBH) merger detected via gravitational waves (GWs) remains a challenge due to the absence of electromagnetic counterparts and the large localization volumes produced by current-generation detectors. A confident host association would provide stellar population properties to constrain BBH formation channels and enable measurements of cosmological parameters such as the Hubble constant, H0. We simulate BBH mergers in nearby (z<0.25) host galaxies to evaluate the feasibility of host identification with future GW detector networks, including configurations with the planned LIGO-India detector and third-generation detectors such as the Einstein Telescope (ET) and Cosmic Explorer (CE). We construct two injection grids to explore variations in BBH mass, distance, and directional sensitivity, and infer localization volumes using the Fisher Information Matrix (FIM)-based parameter estimation implemented through BILBY. To assess the prospects for unique host identification, we introduce a set of diagnostics: theoretical comoving volume thresholds for galaxies of a given stellar mass, derived from galaxy stellar mass functions, a metallicity-based volume threshold motivated by progenitor environment models, stellar mass fractions to quantify candidate host prominence, and the probability of chance alignment (p_c). These metrics provide ways to evaluate host associations and constrain BBH formation channels. We find that future networks that include ET and CE localize BBH mergers to volumes smaller than those theoretical thresholds, implying potentially unique host identification, out to ~1000 Mpc at a rate of ~100 yr^{-1}. While associations for individual events may remain uncertain, our framework is well-suited to population-level analyses, enabling constraints on BBH formation scenarios in the era of next-generation GW detector networks.

astro-ph.HE↗

arXiv2026

RNLE: Residual neural likelihood estimation and its application to gravitational-wave astronomy

Simulation-based inference provides a powerful framework for Bayesian inference when the likelihood is analytically intractable or computationally prohibitive. By leveraging machine-learning techniques and neural density estimators, it enables flexible likelihood or posterior modeling directly from simulations. We introduce Residual Neural Likelihood Estimation (RNLE), a modification of Neural Likelihood Estimation (NLE) that learns the likelihood of non-Gaussian noise in gravitational-wave detector data. Exploiting the additive structure of the signal and noise generation processes, RNLE directly models the noise distribution, substantially reducing the number of simulations required for accurate parameter estimation and improving robustness to realistic noise artifacts. The performance of RNLE is demonstrated using a toy model, simulated gravitational-wave signals, and real detector noise from ground based interferometers. Even in the presence of loud non-Gaussian transients, glitches, we show that RNLE can achieve reliable parameter recovery when trained on appropriately constructed datasets. We further assess the stability of the method by quantifying the variability introduced by retraining the conditional density estimator on statistically identical datasets with different optimization seeds, referred to as training noise. This variability can be mitigated through an ensemble approach that combines multiple RNLE models using evidence-based weighting. An implementation of RNLE is publicly available in the sbilby package, enabling its deployment within gravitational-wave astronomy and a broad range of scientific applications requiring flexible, simulation-based likelihood estimation.

gr-qc↗

arXiv2025

Reconstructing and resampling: a guide to utilising posterior samples from gravitational wave observations

The LIGO, Virgo, and KAGRA (LVK) gravitational-wave observatories have opened new scientific research in astrophysics, fundamental physics, and cosmology. The collaborations that build and operate these observatories release the interferometric strain data as well as a catalogue of observed signals with accompanying Bayesian posterior distributions. These posteriors, in the form of equally-weighted samples, form a dataset that is used by a multitude of further analyses seeking to constrain the population of merging black holes, identify lensed pairs of signals, and much more. However, many of these analyses rely, often implicitly, on the ability to reconstruct the likelihood and prior from the inputs to the analysis and apply resampling (a statistical technique to generate new samples varying the underlying analysis assumptions). In this work, we first provide a guide on how to reconstruct and modify the posterior density accurately from the inputs for analyses performed with the Bilby inference library. We then demonstrate and compare resampling techniques to produce new posterior sample sets and discuss Pareto-smoothing to improve the efficiency. Finally, we provide examples of how to use resampling to study observed gravitational-wave signals. We hope this guide provides a useful resource for those wishing to use open data products from the LVK for gravitational-wave astronomy.

gr-qc↗

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

Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification

Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine learning-driven approach that combines results from individual pipelines and utilises conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multi-pipeline detections, such as the sub-threshold binary neutron star candidate GW200311_103121.

gr-qc↗

arXiv2025

Observation of discontinuities in the periodic modulation of PSR B1828-11

PSR B1828-11 is a radio pulsar that undergoes periodic modulations (~500 days) of its spin-down rate and beam width, providing a valuable opportunity to understand the rotational dynamics of neutron stars. The periodic modulations have previously been attributed to planetary companion(s), precession, or magnetospheric effects and have several interesting features: they persist over 10 cycles, there are at least two harmonically related components, and the period is decreasing at a rate of about 5 days per cycle. PSR B1828-11 also experienced a glitch, a sudden increase in its rotation frequency, at 55 040.9 Modified Julian Day(MJD). By studying the interaction of the periodic modulations with the glitch, we seek to find evidence to distinguish explanations of the periodic modulation. Using a phenomenological model, we analyse a recently published open data set from Jodrell Bank Observatory, providing the longest and highest resolution measurements of the pulsar's spin-down rate data. Our phenomenological model consists of step changes in the amplitude, modulation frequency, and phase of the long-term periodic modulation and the usual spin-down glitch behaviour. We find clear evidence with a (natural-log) Bayes factor of 1486 to support that not only is there a change to these three separate parameters but that the shifts occur before the glitch. Finally, we also present model-independent evidence which demonstrates visually how and when the modulation period and amplitude change. Discontinuities in the modulation period are difficult to explain if a planetary companion sources the periodic modulations, but we conclude with a discussion on the insights into precession and magnetospheric switching.

astro-ph.HE↗

arXiv2025

Transdimensional inference for gravitational-wave astronomy with Bilby

It has become increasingly useful to answer questions in gravitational-wave astronomy using transdimensional models where the number of free parameters can be varied depending on the complexity required to fit the data. Given the growing interest in transdimensional inference, we introduce a new package for the Bayesian inference Library (Bilby) called tBilby. The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when we seek to test theoretically uncertain predictions described by phenomenological models. For example, bursts of gravitational waves can be modelled using a superposition of N wavelets where N is itself a free parameter. Short pulses are modelled with small values of N whereas longer, more complicated signals are represented with a large number of wavelets stitched together. Other transdimensional models have found use describing instrumental noise and the population properties of gravitational-wave sources. We provide a few demonstrations of tBilby, including fitting the gravitational-wave signal GW150914 with a superposition of N sine-Gaussian wavelets. We outline our plans to further develop the tbilby code suite for a broader range of transdimensional problems.

gr-qc↗

arXiv2024

Classification uncertainty for transient gravitational-wave noise artefacts with optimised conformal prediction

With the increasing use of Machine Learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification. When taking a measurement it is not enough to provide the result, we also have to declare how confident we are in the measurement. This is also true when the results are obtained from a ML algorithm, and arguably more so since the internal workings of ML algorithms are often less transparent compared to traditional statistical methods. Additionally, many ML algorithms do not provide uncertainty estimates and auxiliary algorithms must be applied. Conformal Prediction (CP) is a framework to provide such uncertainty quantifications for ML point predictors. In this paper, we explore the use and properties of CP applied in the context of glitch classification in gravitational wave astronomy. Specifically, we demonstrate the application of CP to the Gravity Spy glitch classification algorithm. CP makes use of a score function, a nonconformity measure, to convert an algorithm's heuristic notion of uncertainty to a rigorous uncertainty. We use the application on Gravity Spy to explore the performance of different nonconformity measures and optimise them for our application. Our results show that the optimal nonconformity measure depends on the specific application, as well as the metric used to quantify the performance.

gr-qc↗

arXiv2024

Comparing gravitational waveform models for binary black hole mergers through a hypermodels approach

The inference of source parameters from gravitational-wave signals relies on theoretical models that describe the emitted waveform. Different model assumptions on which the computation of these models is based could lead to biases in the analysis of gravitational-wave data. In this work, we sample directly on four state-of-the-art binary black hole waveform models from different families, in order to investigate these systematic biases from the 13 heaviest gravitational-wave sources with moderate to high signal-to-noise ratios in the third Gravitational-Wave Transient Catalog (GWTC- 3). All models include spin-precession as well as higher-order modes. Using the "hypermodels" technique, we treat the waveform models as one of the sampled parameters, therefore directly getting the odds ratio of one waveform model over another from a single parameter estimation run. From the joint odds ratio over all 13 sources, we find the model NRSur7dq4 to be favoured over SEOBNRv4PHM, with an odds ratio of 29.43; IMRPhenomXPHM and IMRPhenomTPHM have an odds ratio, respectively, of 4.70 and 5.09 over SEOBNRv4PHM. However, this result is mainly determined by three events that show a strong preference for some of the models and that are all affected by possible data quality issues. If we do not consider these potentially problematic events, the odds ratio do not exhibit a significant preference for any of the models. Although further work studying a larger set of signals will be needed for robust quantitative results, the presented method highlights one possible avenue for future waveform model development.

gr-qc↗

arXiv2024

Maximum Entropy Spectral Analysis: an application to gravitational waves data analysis

The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes' maximum entropy principle, allowing the spectrum of a stochastic process to be inferred using the coefficients of an autoregressive process AR($p$) of order $p$. A closed-form recursive solution provides estimates for both the autoregressive coefficients and the order $p$ of the process. We provide a ready-to-use implementation of this algorithm in a Python package called \texttt{memspectrum}, characterized through power spectral density (PSD) analysis on synthetic data with known PSD and comparisons of different criteria for stopping the recursion. Additionally, we compare the performance of our implementation with the ubiquitous Welch algorithm, using synthetic data generated from the GW150914 strain spectrum released by the LIGO-Virgo-Kagra collaboration. Our findings indicate that Burg's method provides PSD estimates with systematically lower variance and bias. This is particularly manifest in the case of a small (O($5000$)) number of data points, making Burg's method most suitable to work in this regime. Since this is close to the typical length of analysed gravitational waves data, improving the estimate of the PSD in this regime leads to more reliable posterior profiles for the system under study. We conclude our investigation by utilising MESA, and its particularly easy parametrisation where the only free parameter is the order $p$ of the AR process, to marginalise over the interferometers noise PSD in conjunction with inferring the parameters of GW150914.

stat.ME↗

arXiv2024

Calibrating gravitational-wave search algorithms with conformal prediction

In astronomy, we frequently face the decision problem: does this data contain a signal? Typically, a statistical approach is used, which requires a threshold. The choice of threshold presents a common challenge in settings where signals and noise must be delineated, but their distributions overlap. Gravitational-wave astronomy, which has gone from the first discovery to catalogues of hundreds of events in less than a decade, presents a fascinating case study. For signals from colliding compact objects, the field has evolved from a frequentist to a Bayesian methodology. However, the issue of choosing a threshold and validating noise contamination in a catalogue persists. Confusion and debate often arise due to the misapplication of statistical concepts, the complicated nature of the detection statistics, and the inclusion of astrophysical background models. We introduce Conformal Prediction (CP), a framework developed in Machine Learning to provide distribution-free uncertainty quantification to point predictors. We show that CP can be viewed as an extension of the traditional statistical frameworks whereby thresholds are calibrated such that the uncertainty intervals are statistically rigorous and the error rate can be validated. Moreover, we discuss how CP offers a framework to optimally build a meta-pipeline combining the outputs from multiple independent searches. We introduce CP with a toy cosmic-ray detector, which captures the salient features of most astrophysical search problems and allows us to demonstrate the features of CP in a simple context. We then apply the approach to a recent gravitational-wave Mock Data Challenge using multiple search algorithms for compact binary coalescence signals in interferometric gravitational-wave data. Finally, we conclude with a discussion on the future potential of the method for gravitational-wave astronomy.

gr-qc↗

arXiv2024

Comparing advanced-era interferometric gravitational-wave detector network configurations: sky localization and source properties

The expansion and upgrade of the global network of ground-based gravitational wave detectors promises to improve our capacity to infer the sky-localization of transient sources, enabling more effective multi-messenger follow-ups. At the same time, the increase in the signal-to-noise ratio of detected events allows for more precise estimates of the source parameters. This study aims to assess the performance of advanced-era networks of ground-based detectors, focusing on the Hanford, Livingston, Virgo, and KAGRA instruments. We use full Bayesian parameter estimation procedures to predict the scientific potential of a network. Assuming a fixed LIGO configuration, we find that the addition of the Virgo detector is beneficial to the sky localization starting from a binary neutron star horizon distance of 20 Mpc and improves significantly from 40 Mpc onwards for both a single and double LIGO detector network, reducing the inferred mean sky-area by up to 95%. Similarly, the KAGRA detector tightens the constraints, starting from a sensitivity range of 10 Mpc. Looking at highly-spinning binary black holes, we find significant improvements with increasing sensitivity in constraining the intrinsic source parameters when adding Virgo to the two LIGO detectors. Finally, we also examine the impact of the low-frequency cut-off data on the signal-to-noise ratio. We find that existing 20 Hz thresholds are sufficient and propose a metric to monitor this to study detector performance. Our findings quantify how future enhancements in detector sensitivity and network configurations will improve the localization of gravitational wave sources and allow for more precise identification of their intrinsic properties.

gr-qc↗

arXiv2024

Revisiting the evidence for precession in GW200129 with machine learning noise mitigation

GW200129 is claimed to be the first-ever observation of the spin-disk orbital precession detected with gravitational waves (GWs) from an individual binary system. However, this claim warrants a cautious evaluation because the GW event coincided with a broadband noise disturbance in LIGO Livingston caused by the 45 MHz electro-optic modulator system. In this paper, we present a state-of-the-art neural network that is able to model and mitigate the broadband noise from the LIGO Livingston interferometer. We also demonstrate that our neural network mitigates the noise better than the algorithm used by the LIGO-Virgo-KAGRA collaboration. Finally, we re-analyse GW200129 with the improved data quality and show that the evidence for precession is still observed.

gr-qc↗