Search

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.

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., Comment: 12 pages, 7 figures

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., Comment: 18 pages, 20 figures, accepted in Phys. Rev. D

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., Comment: Updating with the journal-reviewed version. 8 pages, 6 figures. Data frame available at DOI: 10.5281/zenodo.10143337

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.

Precision pulsar timing is integral to the detection of the nanohertz stochastic gravitational-wave background as well as understanding the physics of neutron stars. Conventional pulsar timing often uses fixed time and frequency-averaged templates to determine the pulse times of arrival, which can lead to reduced accuracy when the pulse profile evolves over time. We illustrate a dynamic timing method that fits each observing epoch using basis functions. By fitting each epoch separately, we allow for the evolution of the pulse shape epoch to epoch. We apply our method to PSR J1103$-$5403 and demonstrate that it undergoes mode changing, making it the fourth millisecond pulsar to exhibit such behaviour. Our method, which is able to identify and time a single mode, yields a timing solution with a root-mean-square error of 1.343 $\mu \mathrm{s}$, a factor of 1.78 improvement over template fitting on both modes. In addition, the white-noise amplitude is reduced 4.3 times, suggesting that fitting the full data set causes the mode changing to be incorrectly classified as white noise. This reduction in white noise boosts the signal-to-noise ratio of a gravitational-wave background signal for this particular pulsar by 32%. We discuss the possible applications for this method of timing to study pulsar magnetospheres and further improve the sensitivity of searches for nanohertz gravitational waves., Comment: Accepted in MNRAS, 8 pages, 8 figures

The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of binary compact objects. However, many more real GWs are lurking sub-threshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference on the glitch under the assumption it has an astrophysical source (e.g. binary black hole coalescence) results in source parameters that are inconsistent with what is known about the astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalog of both real GW events and glitch contaminants by performing Bayesian inference on their source populations simultaneously. In this paper, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalog belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalog, finding it to be consistent with the actual number of events included., Comment: 13 pages, 10 figures

Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enables detailed analysis of the curved space-time around black holes. With nearly one hundred signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolution and the expansion of our Universe. However, the detectors are afflicted by transient noise artefacts known as "glitches" which contaminate the signals and bias inferences. Of the 90 signals detected to date, 18 were contaminated by glitches. This feasibility study explores a new approach to transient gravitational-wave data analysis using Gaussian processes, which model the underlying physics of the glitch-generating mechanism rather than the explicit realisation of the glitch itself. We demonstrate that if the Gaussian process kernel function can adequately model the glitch morphology, we can recover the parameters of simulated signals. Moreover, we find that the Gaussian processes kernels used in this work are well-suited to modelling long-duration glitches which are most challenging for existing glitch-mitigation approaches. Finally, we show how the time-domain nature of our approach enables a new class of time-domain tests of General Relativity, performing a re-analysis of the inspiral-merger-ringdown test on the first observed binary black hole merger. Our investigation demonstrates the feasibility of the Gaussian processes as an alternative to the traditional framework but does not yet establish them as a replacement. Therefore, we conclude with an outlook on the steps needed to realise the full potential of the Gaussian process approach., Comment: 12 pages, published in MNRAS

There are at present ${\cal O}(100)$ gravitational-wave candidates from compact binary mergers reported in the astronomical literature. As detector sensitivities are improved, the catalog will swell in size: first to ${\cal O}(1000)$ events in the A+ era and then to ${\cal O}(10^6)$ events in the era of third-generation observatories like Cosmic Explorer and the Einstein Telescope. Each event is analyzed using Bayesian inference to determine properties of the source including component masses, spins, tidal parameters, and the distance to the source. These inference products are the fodder for some of the most exciting gravitational-wave science, enabling us to measure the expansion of the Universe with standard sirens, to characterise the neutron star equation of state, and to unveil how and where gravitational-wave sources are assembled. In order to maximize the science from the coming deluge of detections, we introduce GWCloud, a searchable repository for the creation and curation of gravitational-wave inference products. It is designed with five pillars in mind: uniformity of results, reproducibility of results, stability of results, access to the astronomical community, and efficient use of computing resources. We describe how to use GWCloud with examples, which readers can replicate using the companion code to this paper. We describe our long-term vision for GWCloud., Comment: 14 pages, 9 figures

Radio pulsar glitches probe far-from-equilibrium processes involving stress accumulation and relaxation in neutron star interiors. Previous studies of glitch rates have focused on individual pulsars with as many recorded glitches as possible. In this work we analyze glitch rates using all available data including objects that have glitched never or once. We assume the glitch rate follows a homogeneous Poisson process, and therefore exclude pulsars which exhibit quasiperiodic glitching behavior. Calculating relevant Bayes factors shows that a model in which the glitch rate $\lambda$ scales as a power of the characteristic age $\tau$ is preferred over models which depend arbitrarily on powers of the spin frequency $\nu$ and/or its time derivative $\dot{\nu}$. For $\lambda = A (\tau/\tau\vref)^{-\gamma}$, where $\tau_{\rm ref}=1\ {\rm yr}$ is a reference time, the posterior distributions are unimodal with $A=\ModelAAglitch\ \rm{yr}^{-1}$, and $\gamma=\ModelAgammaglitch$. Importantly, the data exclude with 99\% confidence the possibility $\gamma=1$ canvassed in the literature. When objects with zero recorded glitches are included, the age-based rate law is still preferred and the posteriors change to give $A=\ModelAAall\ \rm{yr}^{-1}$, and $\gamma=\ModelAgammaall$. The updated estimates still support increased glitch activity for younger pulsars, while demonstrating that the large number of objects with zero glitches contain important statistical information about the rate, provided that they are part of the same population as opposed to a disjoint population which never glitches for some unknown physical reason., Comment: 18 pages, 11 figures. Accepted for publication in MNRAS

The upcoming Laser Interferometer Space Antenna (LISA) will detect a large gravitational-wave foreground of Galactic white dwarf binaries. These sources are exceptional for their probable detection at electromagnetic wavelengths, some long before LISA flies. Studies in both gravitational and electromagnetic waves will yield strong constraints on system parameters not achievable through measurements of one messenger alone. In this work, we present a Bayesian inference pipeline and simulation suite in which we study potential constraints on binaries in a variety of configurations. We show how using LISA detections and parameter estimation can significantly improve constraints on system parameters when used as a prior for the electromagnetic analyses. We also provide rules of thumb for how current measurements will benefit from LISA measurements in the future., Comment: 8 pages, 5 figures, accepted to MNRAS

Gravitational waves from the collision of binary neutron stars provide a unique opportunity to study the behaviour of supranuclear matter, the fundamental properties of gravity, and the cosmic history of our Universe. However, given the complexity of Einstein's Field Equations, theoretical models that enable source-property inference suffer from systematic uncertainties due to simplifying assumptions. We develop a hypermodel approach to compare and measure the uncertainty gravitational-wave approximants. Using state-of-the-art models, we apply this new technique to the binary neutron star observations GW170817 and GW190425 and the sub-threshold candidate GW200311_103121. Our analysis reveals subtle systematic differences between waveform models, and a frequency-dependence study suggests that this is due to the treatment of the tidal sector. This new technique provides a proving ground for model development, and a means to identify waveform-systematics in future observing runs where detector improvements will increase the number and clarity of binary neutron star collisions we observe., Comment: 10 pages, 5 figures, 1 table. Published Nature Astronomy

Gravitational-wave observations of binary neutron star mergers and their electromagnetic counterparts provide an independent measurement of the Hubble constant, $H_0$, through the standard-sirens approach. Current methods of determining $H_0$, such as measurements from the early universe and the local distance ladder, are in tension with one another. If gravitational waves are to break this tension a thorough understanding of systematic uncertainty for gravitational-wave observations is required. To accurately estimate the properties of gravitational-wave signals measured by LIGO and Virgo, we need to understand the characteristics of the detectors noise. Non-gaussian transients in the detector data and rapid changes in the instrument, known as non-stationary noise, can both add a systematic uncertainty to inferred results. We investigate how non-stationary noise affects the estimation of the luminosity distance of the source, and therefore of $H_0$. Using a population of 200 simulated binary neutron star signals, we show that non-stationary noise can bias the estimation of the luminosity distance by up to 2.4\%. However, only $\sim$15\% of binary neutron star signals would be affected around their merger time with non-stationary noise at a similar level to that seen in the first half of LIGO-Virgo's third observing run. Comparing the expected bias to other systematic uncertainties, we argue that non-stationary noise in the current generation of detectors will not be a limiting factor in resolving the tension on $H_0$ using standard sirens. Although, evaluating non-stationarity in gravitational-wave data will be crucial to obtain accurate estimates of $H_0$., Comment: 10 pages, 5 figures, 1 table

The two interferometric LIGO gravitational-wave observatories provide the most sensitive data to date to study the gravitational-wave Universe. As part of a global network, they have just completed their third observing run in which they observed many tens of signals from merging compact binary systems. It has long been known that a limiting factor in identifying transient gravitational-wave signals is the presence of transient non-Gaussian noise, which reduce the ability of astrophysical searches to detect signals confidently. Significant efforts are taken to identify and mitigate this noise at the source, but its presence persists, leading to the need for software solutions. Taking a set of transient noise artefacts categorised by the GravitySpy software during the O3a observing era, we produce parameterised population models of the noise projected into the space of astrophysical model parameters of merging binary systems. We compare the inferred population properties of transient noise artefacts with observed astrophysical systems from the GWTC2.1 catalogue. We find that while the population of astrophysical systems tend to have near equal masses and moderate spins, transient noise artefacts are typically characterised by extreme mass ratios and large spins. This work provides a new method to calculate the consistency of an observed candidate with a given class of noise artefacts. This approach could be used in assessing the consistency of candidates found by astrophysical searches (i.e. determining if they are consistent with a known glitch class). Furthermore, the approach could be incorporated into astrophysical searches directly, potentially improving the reach of the detectors, though only a detailed study would verify this., Comment: 15 pages, submitted to Phys. Rev. D

The detection of an intermediate-mass black hole population ($10^2-10^6$ $M_{\odot}$) will provide clues to their formation environments (e.g., disks of active galactic nuclei, globular clusters) and illuminate a potential pathway to produce supermassive black holes. Ground-based gravitational-wave detectors are sensitive to mergers that can form intermediate-mass black holes weighing up to $\sim 450\ M_{\odot}$. However, ground-based detector data contain numerous incoherent short duration noise transients that can mimic the gravitational-wave signals from merging intermediate-mass black holes, limiting the sensitivity of searches. Here we follow-up on binary black hole merger candidates using a ranking statistic that measures the coherence or incoherence of triggers in multiple-detector data. We use this statistic to rank candidate events, initially identified by all-sky search pipelines, with lab-frame total masses $>55\ M_{\odot}$ using data from LIGO's second observing run. Our analysis does not yield evidence for new intermediate-mass black holes. However, we find support for eight stellar-mass binary black holes not reported in the first LIGO-Virgo gravitational wave transient catalog GWTC-1, seven of which have been previously reported by other catalogs., Comment: 10 pages, 3 figures