arXiv subjects

Charlie Hoy

Publications and source records attributed to Charlie Hoy.

At least 19 recordsLinked to original sources

arXiv2026

Noisy pipelines II: Non-parametric Noise Inference for LISA

The Laser Interferometer Space Antenna (LISA) is expected to observe gravitational-wave signals from massive black hole binaries, extreme mass ratio inspirals, and more. Despite the significant scientific potential, accurately extracting these signals from the data will only be possible if LISA's instrumental noise is understood: poor noise modelling will result in lost signals, biased parameter estimates, and incorrect astrophysical insights. Modelling LISA's instrumental noise is more challenging than its ground-based counterpart since the data is highly correlated, meaning that the noise covariance matrix can no longer be assumed diagonal in frequency and in the different data streams. In this work, we present an initial approach to perform non-parametric noise inference for LISA that requires no prior knowledge of the LISA observatory. By fitting the full noise covariance matrix with a combination of splines and inverse Lorentzians, we show that our model can estimate the power and cross spectral densities for LISA from 7 days of simulated noise-only data. We find that the median estimate from our non-parametric model whitens the data according to the expected unit normal distribution. We envision that our non-parametric model can be used as part of the global fit, as well as in combination with modelled approaches to identify, quantify, and constrain the frequency dependence of unknown noise artifacts.

gr-qc↗

arXiv2026

Noisy Pipelines I: An end-to-end pipeline to perform multivariate noise inference for LISA

Instrumental data from the Laser Interferometer Space Antenna (LISA) must be post-processed prior to any global-fit based analysis. For example, it is critical to filter, downsample, segment and window the data in order to perform inference of both the signal and noise parameters in the frequency domain. Each step alters the noise process, requiring a correction to the noise covariance matrix that describes the underlying data stream. We present $\texttt{lisa-noise}$, a JAX-based noise inference pipeline that returns a spectral estimate of the noise process subject to post-processing effects. We fit splines to the underlying secondary noises and fit the full multivariate $3 \times 3$ covariance matrix of the Michelson channels. We assume unequal and non-constant arm-lengths throughout. The drift of the zeros of the transfer functions, a non-stationary effect, is captured by averaging the transfer functions over a number of epochs within each segment. On 30 days of conditioned simulated data, a likelihood evaluation costs $3\,$ms on a current laptop for a 7 day segment. A gradient-based fit from a tight prior recovers the injected spectra in a fraction of a second. A multi-start search over large priors $\pm 10$ in $\log_{10}$ centred on the simulated truth obtains a noise estimate consistent with the data, verified by Bayesian techniques. The fitted covariance whitens the conditioned data in every band and through every zero of the transfer functions, in a form that global-fit pipelines can use directly.

gr-qc↗

arXiv2026

Leveraging rapid parameter estimates for efficient gravitational-wave Bayesian inference via posterior repartitioning

Gravitational-wave astronomy typically relies on rigorous, computationally expensive Bayesian analyses. Several methods have also been developed to perform rapid, approximate Bayesian inference. We present a novel approach to leverage the results of these low-latency analyses to accelerate the final inference, whilst ensuring that the Bayesian prior remains independent of the data. By combining the fast constraints from the \texttt{simple-pe} algorithm with the nested sampling acceleration technique of posterior repartitioning, we demonstrate that our method can guide the nested sampler towards the most probable regions of parameter space more efficiently for signal-to-noise ratios (SNR) greater than 20, while mathematically guaranteeing that the final inference is identical to that of a standard, uninformed analysis. We validate the method through an injection study on signals with SNR $<$ 150, demonstrating that it produces statistically robust and unbiased results whilst providing speedups of up to 210\%, with a mean speedup of 34\% for SNRs $>$ 20. Importantly, we show that the performance gain provided by our method scales with SNR, establishing it as a powerful technique to mitigate the cost of analysing signals from current and future gravitational-wave observatories.

gr-qc↗

arXiv2026

Compactness Inference in Gravitational-Wave Mergers with PhenomDECO: Catalog Benchmarks and Robustness Diagnostics

Several gravitational wave (GW) observations have been identified as binary black hole (BBH) mergers, including systems with component masses that challenge typical formation scenarios. These observations motivate broader tests of whether the detected sources are consistent with this interpretation. We address this question using~\deco~, an existing phenomenological extension of a BBH model that uses an effective compactness parameter to characterize departures from the expected merger morphology. Applying this model to all high-significance BBH events from GWTC-3, we establish~\deco~as a robust test of the nature of compact binaries. In preliminary analyses we identify three recurring posterior morphologies: (i) near-Gaussian peaks consistent with the BBH expectation $C\sim0.5$, seen in 60\% of events; (ii) posteriors with additional high-compactness support $(C\ge0.8)$; and (iii) dominant low-compactness modes near $C\sim0.15$ in $\sim 20\%$ of cases. For the latter, the low-compactness modes disappear when the data, especially from Livingston, are analyzed from a higher starting frequency, indicating sensitivity to low-frequency noise artefacts. We further use time--frequency residuals, computed after subtracting maximum-likelihood BBH and~\deco~waveforms from the strain data, to assess if the data is better described by a compactness-based deformation. With this analysis, we conclude that all of the GWTC-3 observations that we have considered are indeed consistent with BBH sources. The exception is the high-mass GW231123 signal, for which data from \emph{both} detectors must be analyzed above 50Hz to remove a low-compactness mode. This study shows that low-frequency data treatment is crucial before attributing apparent deviations from BBH expectations to exotic physics, and provides a benchmark for compactness-based tests of merger morphology in current and future GW detections.

gr-qc↗

arXiv2026

Establishing Compactness as a Population Observable in Gravitational-Wave Astronomy

Classically, black holes (BHs) are the most compact objects predicted in nature with C=0.5 in the Schwarzschild limit; C is defined as the mass-to-radius ratio in geometric units. In this work we perform a novel measurement on the nature of putative BH mergers in the gravitational wave (GW) data by directly probing the binary's closest approach through an effective compactness parameter. We confidently show all such high-significance signals in GWTC-3 are consistent with the BH hypothesis for the first time. Our hierarchical analysis yields $C_{\rm eff} = 0.5^{+0.3}_{-0.1}$, and we further limit the merger rate of low-compactness exotic binaries to $< 0.7\,{\rm Gpc}^{-3}\,{\rm yr}^{-1}$. This work establishes compactness as a key observable in GW astronomy.

gr-qc↗

arXiv2026

Inference with finite time series II: the window strikes back

Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-KAGRA collaboration, it is conventional to include a term to account for power loss due to this window in the likelihood function. We show that the inclusion of this factor leads to biased inference. The simplest solution to this, omitting the factor, leads to unbiased posteriors and Bayes factor estimates provided the window does not suppress the signal for signal-to-noise ratios $\lesssim O(100)$, but unreliable estimates of the absolute likelihood. Instead, we propose a multi-stage method that yields consistent estimates for the absolute likelihood in addition to unbiased posterior distributions and Bayes factors for signal-to-noise ratios $\lesssim O(1000)$. Additionally, we demonstrate that the commonly held wisdom that using rectangular windows necessarily leads to biased inference is incorrect.

gr-qc↗

arXiv2026

The impact of precession and higher-order multipoles for gravitational wave cosmological inference

Gravitational-wave astronomy presents an exciting opportunity to provide an independent measurement of the expansion rate of the Universe. By combining inferences for the binary component masses and luminosity distances from individual observations, it is possible to infer $H_0$ without direct electromagnetic counterparts or galaxy catalogs. However, this relies on theoretical gravitational-wave models, which are known to be incomplete descriptions of the full predictions of general relativity. Although the accuracy of our models are improving, they are also becoming increasingly expensive as additional phenomena are incorporated. In this work, we demonstrate that there is no significant advantage in including spin-precession and higher-order multipole moments when inferring $H_0$ via the mass spectrum method for current and near-future gravitational-wave event numbers. Even when simulating a population of highly precessing and preferentially asymmetric-mass-ratio binaries, we show that the inferred $H_0$ posterior changes minimally. Using a simpler, less accurate model, achieves comparable $H_0$ estimates with six times less computational cost (on average). Using computationally cheaper models for single event inference may become crucial as thousands of gravitational-wave observations are expected to be detected in the near future.

astro-ph.CO↗

arXiv2026

A parametric signal plus noise inference framework for short duration non-Gaussian noise transients

Gravitational waves are now routinely detected with ground-based observatories, and, through a process known as Bayesian inference, their source properties are inferred. However, terrestrial noise artifacts, often referred to as glitches, commonly overlap astrophysical signals. This invalidates a fundamental assumption of gravitational wave analyses: the noise is no longer stationary and Gaussian. As a result, traditional techniques can provide biased inferences in realistic data. One method for mitigating the effect of glitches is to jointly analyse both the signal and noise in a single framework. In this work, we introduce bilby-antiglitch to infer the astrophysical signal properties in non-Gaussian noise. By additionally including a quasi-physical glitch model to describe short duration non-Gaussian noise transients, we show that unlike traditional techniques, we infer the true source properties of simulated signals contaminated with loud glitches. We also show that bilby-antiglitch prevents false violation claims of General Relativity, and validates the exceptional nature of gravitational wave signals in spurious data.

gr-qc↗

arXiv2026

Reconsidering the consistent use of precessing, higher order multipole models for gravitational wave analyses

The growing number of gravitational-wave (GW) observations allows for constraints to be placed on the underlying population of black holes; current estimates show that black hole spins are small, with binaries more likely to have comparable component masses. Since general relativistic effects, such as spin-induced orbital precession and higher order multipole moments, are more likely to be observed for asymmetric binary systems, a direct measurement remains unlikely. Nevertheless, we continue to consistently probe these effects by performing Bayesian inference with our most accurate and computationally expensive models. As the number of GW detections increases, it may soon become infeasible to consistently use these models for analyses. In this paper, we provide a selection criterion that determines when less accurate and computationally cheaper models can be used without giving biased estimates for the population properties of black holes in the Universe. We show that when using our selection criterion, comparable estimates can be obtained for the underlying mass and spin distribution of black holes for a simulated ``worst-case'' scenario population, while reducing the overall cost of performing Bayesian inference on our population by $\sim 20\%$. We anticipate a reduction of up to $78\%$ in the overall cost for an astrophysically motivated population, since there are fewer events with observable spin-precession and higher order multipole power.

gr-qc↗

arXiv2026

A universal framework to identify eccentric binary mergers: GW200105 case study

Orbital eccentricity in gravitational-wave signals from merging compact object binaries is a powerful indicator of their formation channel. Several binary black hole mergers and a neutron star--black hole merger have been reported to exhibit signs of eccentricity, but which events are identified and the significance of the eccentricity differs between studies. Measurements of eccentricity can change depending on the choice of prior. The choice of prior is subtle: eccentricity is commonly measured at an arbitrary reference frequency, which varies from study to study. We use the candidate eccentric neutron star--black hole merger GW200105_162426 as a case study, employing a range of priors and reference frequencies, and find the results to be strongly prior-driven. We show that the varied results reported across different studies can be partially reconciled by accounting for the evolution of eccentricity with reference frequency. In order to make conclusive statements about eccentricity, we propose a detection statistic that does not depend on reference frequency, and which marginalises over astrophysically-motivated distributions in eccentricity. Using this detection statistic, we find reduced support for the eccentric hypothesis for GW200105_162426: we obtain a natural log Bayes factor ln B $\leq$ 0.9 comparing the eccentric, aligned-spin hypothesis to the quasi-circular, precessing hypothesis. Our results cast doubt on the eccentric interpretation of GW200105_162426 and underscore the importance of modelling the astrophysical distributions of eccentricity in nature.

astro-ph.HE↗

arXiv2026

Constraining the neutron star-black hole merger rate

Current template-based gravitational-wave searches for compact binary mergers neglect the general relativistic phenomenon of spin-induced orbital precession. Owing to their asymmetric masses, gravitational-waves from neutron star-black hole (NSBH) binaries are prime candidates for displaying strong imprints of spin-precession. Current searches may therefore miss a significant fraction of the astrophysical population, and the detected NSBH population may be significantly suppressed or biased. Here we report the most sensitive search for NSBH binaries to date by including spin-precession for the first time. We analyze data from the entirety of the third LIGO--Virgo--KAGRA gravitational-wave observing run and show that when accounting for spin-precession, our search is up to 100% more sensitive than the search techniques currently adopted by the LIGO--Virgo--KAGRA collaboration (for systems with strong precessional effects). This allows us to more tightly constrain the rate of NSBH mergers in the local Universe. When focusing on a potentially precessing subpopulation of NSBH mergers, the lack of observed candidates allows us to place an upper limit on the merger rate of $R_{90} = 79\, \mathrm{Gpc}^{-3}\mathrm{yr}^{-1}$ with 90% confidence. We then show that if there is no preferred direction of component spin, the overall rate of NSBH mergers is on average 16% smaller than previously believed. Finally, we report four new subthreshold NSBH candidates, all with strong imprints of spin precession, but note that these are most likely to be of terrestrial origin.

hep-ex↗

arXiv2025

Waging a Campaign: Results from an Injection-Recovery Study involving 35 numerical Relativity Simulations and three Waveform Models

We present Bayesian inference results from an extensive injection-recovery campaign to test the validity of three state of the art quasicircular gravitational waveform models: \textsc{SEOBNRv5PHM}, \textsc{IMRPhenomTPHM}, \textsc{IMRPhenomXPHM}, the latter with the \textsc{SpinTaylorT4} implementation for its precession dynamics. We analyze 35 strongly precessing binary black hole numerical relativity simulations with all available harmonic content. Ten simulations have a mass ratio of $4:1$ and five, mass ratio of $8:1$. Overall, we find that \textsc{SEOBNRv5PHM} is the most consistent model to numerical relativity, with the majority of true source properties lying within the inferred 90\% credible interval. However, we find that none of the models can reliably infer the true source properties for binaries with mass ratio $8:1$ systems. We additionally conduct inspiral-merger-ringdown (IMR) consistency tests to determine if our chosen state of the art waveform models infer consistent properties when analysing only the inspiral (low frequency) and ringdown (high frequency) portions of the signal. For the simulations considered in this work, we find that the IMR consistency test depends on the frequency that separates the inspiral and ringdown regimes. For two sensible choices of the cutoff frequency, we report that \textsc{IMRPhenomXPHM} can produce false GR deviations. Meanwhile, we find that \textsc{IMRPhenomTPHM} is the most reliable model under the IMR consistency test. Finally, we re-analyze the same 35 simulations, but this time we incorporate model accuracy into our Bayesian inference. Consistent with the work in Hoy et al. 2024 [arXiv: 2409.19404], we find this approach generally yields more accurate inferred properties for binary black holes with less biases compared to methods that combine model-dependent posterior distributions based on their evidence, or with equal weight.

gr-qc↗

arXiv2025

The Missing Multipole Problem: Investigating biases from model starting frequency in gravitational-wave analyses

Our ability to infer the true source properties of colliding black holes from gravitational wave observations requires not only accurate waveform models but also their correct use. A key property when evaluating time-domain models is when to start the waveform: choosing a time that is too late can omit low-frequency power from higher order multipoles. By focusing on binary systems with total mass $\ge 200 \, M_{\odot}$, we show that current detectors are sensitive to this missing power and biased source properties can be obtained. We show that for systems with total mass $\lesssim 300 \, M_{\odot}$, mass ratio $\gtrsim 0.33$, and signal-to-noise ratio $ρ\gtrsim 20$, templates starting at $20 \, \mathrm{Hz}$ recover biased source properties. As the total mass increases, and the component masses become more asymmetric, templates starting from $13 \, \mathrm{Hz}$ recover biased properties. If the gravitational-wave signal is observed at signal-to-noise ratio $ρ< 20$, time-domain models can start from $20\, \mathrm{Hz}$ as statistical uncertainties dominate.

gr-qc↗

arXiv2025

Incorporation of model accuracy in gravitational wave Bayesian inference

Inferring the properties of colliding black holes from gravitational-wave observations is subject to systematic errors arising from modelling uncertainties. Although the accuracy of each model can be calculated through comparison to theoretical expectations from general relativity, Bayesian analyses are yet to incorporate this information. As such, a mixture model is typically used where results obtained with different gravitational-wave models are combined with either equal weight, or based on their relative Bayesian evidence. In this work we present a novel method to incorporate the accuracy of multiple models in gravitational-wave Bayesian analyses. By analysing simulated gravitational-wave signals in zero-noise, we show that our technique uses $30\%$ less computational resources, and more faithfully recovers the true parameters than existing techniques. We further apply our method to a real gravitational-wave signal and, when assuming the binary black hole hypothesis, demonstrate that the source of GW191109_010717 has unequal component masses, with the primary having a $69\%$ probability that it lies above the maximum black hole mass from stellar collapse. We envisage that this method will become an essential tool within ground-based gravitational-wave astronomy.

gr-qc↗

arXiv2025

PhenomXPNR: An improved gravitational wave model linking precessing inspirals and NR-calibrated merger-ringdown

We present the frequency-domain quasi-circular precessing binary-black-hole model PhenomXPNR. This model combines the most precise available post-Newtonian description of the evolution of the precession dynamics through inspiral with merger-ringdown model informed by numerical relativity. This, along with a phenomenological model of the dominant multipole asymmetries, results in the most accurate and complete representation of the physics of precessing binaries natively in the frequency-domain to date. All state-of-the-art precessing models show bias when inferring binary parameters in certain regions of the parameter space. We demonstrate that the developments presented ensure that for some precessing systems PhenomXPNR shows the least degree of bias. Further, as a phenomenological, frequency-domain model, PhenomXPNR remains one of the most computationally efficient models available and is therefore well-suited to the era of gravitational-wave astronomy with its ever growing rate of detected signals.

gr-qc↗

arXiv2025

On the use and interpretation of signal-model indistinguishability measures for gravitational-wave astronomy

The difference ("mismatch") between two gravitational-wave (GW) signals is often used to estimate the signal-to-noise ratio (SNR) at which they will be distinguishable in a measurement or, alternatively, when the errors in a signal model will lead to biased measurements. It is well known that the standard approach to calculate this "indistinguishability SNR" is too conservative: a model may fail the criterion at a given SNR, but not necessarily incur a biased measurement of any individual parameters. This problem can be solved by taking into account errors orthogonal to the model space (which therefore do not induce a bias), and calculating indistinguishability SNRs for individual parameters, rather than the full $N$-dimensional parameter space. We illustrate this approach with the simple example of aligned-spin binary-black-hole signals, and calculate accurate estimates of the SNR at which each parameter measurement will be biased. In general biases occur at much higher SNRs than predicted from the standard mismatch calculation. Which parameters are most easily biased depends sensitively on the details of a given waveform model, and the location in parameter space, and in some cases the bias SNR is as high as the conservative estimate. We also illustrate how the parameter bias SNR can be used to robustly specify waveform accuracy requirements for future detectors.

gr-qc↗

arXiv2025

Rarity of precession and higher-order multipoles in gravitational waves from merging binary black holes

The latest binary black hole population estimates argue for a subpopulation of unequal component mass binaries with spins that are likely small but isotropically distributed. This implies a non-zero probability of detecting spin-induced orbital precession and higher order multipole moments in the observed gravitational-wave signals. In this work we directly calculate the probability for precession and higher-order multipoles in each significant gravitational-wave candidate observed by the LIGO--Virgo--KAGRA collaborations (LVK). We find that only one event shows substantial evidence for precession: GW200129_065458, and two events show substantial evidence for higher-order multipoles: GW190412 and GW190814; any evidence for precession and higher-order multipole moments in other gravitational-wave signals is consistent with random fluctuations caused by noise. We then compare our observations with expectations from population models, and confirm that current population estimates from the LVK accurately predict the number of observed events with significant evidence for precession and higher-order multipoles. In particular, we find that this population model predicts that a binary with significant evidence for precession will occur once in every $\sim 50$ detections, and a binary with significant evidence for higher-order multipoles will occur once in every $\sim 70$ observations. However, we emphasise that since substantial evidence for precession and higher-order multipoles have only been observed in three events, any population model that includes a subpopulation of binaries yielding $\sim 2\%$ of events with detectable precession and higher-order multipole moments will likely be consistent with the data.

gr-qc↗

arXiv2024

Premerger observation and characterization of massive black hole binaries

We demonstrate an end-to-end technique for observing and characterizing massive black hole binary signals before they merge with the LISA space-based gravitational-wave observatory. Our method uses a zero-latency whitening filter, originally designed for rapidly observing compact binary mergers in ground-based observatories, to be able to observe signals with no additional latency due to filter length. We show that with minimal computational cost, we are able to reliably observe signals as early as 14 days premerger as long as the signal has accrued a signal-to-noise ratio of at least 8 in the LISA data. We also demonstrate that this method can be used to characterize the source properties, providing early estimates of the source's merger time, chirp mass, and sky localization. Early observation and characterization of massive black holes is crucial to enable the possibility of rapid multimessenger observations, and to ensure that LISA can enter a protected operating period when the merger signal arrives.

hep-ex↗