arXiv subjects

Vivien Raymond

Publications and source records attributed to Vivien Raymond.

At least 19 recordsLinked to original sources

arXiv2026

Simulation-based Inference for Gravitational Waves from Binary Neutron Stars: Application of Summary Data from Heterodyning

Gravitational-wave parameter estimation for binary neutron star (BNS) systems poses severe computational challenges due to the extended signal duration, which can reach several minutes in current detectors. Neural posterior estimation (NPE), a simulation-based inference approach, offers dramatic speedups but requires effective dimensionality reduction of the high-dimensional input data. We present a novel compression strategy based on likelihood-oriented summary statistics derived from the relative binning formalism of Zackay et al. (2018), which compresses raw frequency-domain data into the summary data. The summary data is based on a polynomial approximation of the waveform ratio using frequency banding grounded in post-Newtonian approximation, and directly evaluated with only $O(1000)$ sample points of the waveform. As a result, both the training and storage cost become more efficient than previously reported networks for BNS inference. We train a set of NPE networks on these summary statistics and validate a network against traditional nested sampling over 1024 BNS injections. The network produces well-calibrated posteriors across all source parameters we consider, with Jensen-Shannon divergences (JSD) consistent with numerical noise for most parameters. Although we find that the median JSD for the most inconsistent parameter exceeds $10^{-2}$ bits with current configurations, our results show potential for rapid parameter estimation of the BNS signal.

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arXiv2026

Simulation-based Inference towards Gravitational-wave waveform systematics in Intermediate-Mass Binary Black Holes

Parameter estimation for gravitational-wave signals is computationally demanding due to the high dimensionality of the parameter space and the cost of repeated waveform generation in traditional Bayesian inference. These analyses require on the order of 10^8 likelihood evaluations and waveform generations, resulting in inference times of hours to days per event. Furthermore, discrepancies between waveform models introduce systematic uncertainties that can bias inferred source properties. To address these challenges, we propose a novel framework based on Simulation-based Inference (SBI) and Neural Posterior Estimation (NPE) and apply it to signals from Intermediate-Mass Black Holes (IMBH). In this framework, we train a single amortised neural posterior estimator on a large simulated dataset generated using two state-of-the-art waveform approximants, IMRPhenomXPHM and SEOBNRv5PHM. By treating the waveform model index as a latent variable, the network learns to produce posterior distributions that are naturally marginalized over the discrepancies of the two waveform models. Once trained, the model enables direct posterior sampling in milliseconds per event, eliminating the need for likelihood evaluations while simultaneously accounting for model systematics. We demonstrate that this approach recovers accurate posterior distributions for IMBH signals injected into Gaussian noise, achieving close agreement with traditional nested-sampling results while reducing inference time by several orders of magnitude. Our results show that NPE can robustly incorporate waveform-model systematics within a unified framework, offering a scalable path toward rapid, systematics-aware gravitational-wave inference. Establishing these methods as promising alternatives to classical likelihood-based pipelines for current and future high-mass gravitational-wave observations.

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arXiv2025

Gravitational-wave Observations Suggest Most Black Hole Mergers Form in Triples

The spin-orbit tilt angles $\theta_{1(2)}$ of merging stellar-mass black holes provide key insights into their astrophysical origin. Non-parametric population modelling of The LIGO, Virgo, and KAGRA Collaborations (2025a, arXiv:2508.18083) shows that the spin-orbit tilt distribution of mergers in the latest Gravitational-Wave Transient Catalog 4.0 exhibits a global peak at near-perpendicular directions $\cos\theta_{1(2)}\approx0$. Here, we recover this feature using hierarchical Bayesian inference with parametric models that are tailored to enhance the diagnostic power about astrophysical formation channels. We find that the spin distribution of the low-mass bulk of the binary black hole merger population ($m_1\lesssim 44.3^{+8.7}_{-4.6}\,\rm M_\odot$) can be well-modelled by a dominant Gaussian component that peaks at $\cos\theta_{1(2)}\approx0$, possibly mixed with a subdominant isotropic component. Models that include a component with spins preferentially aligned with the orbit are disfavoured by current data (with Bayes factors $|\Delta\ln\mathcal{B}|\approx1$ to $3$) and constrain its contribution to be likely small ($\xi\sim\mathcal{O}(1)\,\%$), although large contributions cannot yet ruled out with certainty. If these findings are reinforced by more detections, they would challenge any major contribution from the traditional isolated-binary formation scenario yielding closely aligned spins. Instead, the dominant component with near-perpendicular spins matches expectations from the evolution of isolated massive stellar triples in the galactic field, where the Lidov-Kozai effect naturally produces a unique overabundance of mergers with $\cos\theta_{1(2)}\approx0$.

astro-ph.HE↗

arXiv2025

Fast and Precise Spectral Analysis for Dark Matter Searches with LIGO

We introduce a novel logarithmic spectral estimation method for dark matter searches using gravitational-wave detectors, integrating established dark matter search techniques with insights from computer music analysis. By leveraging symmetries between the time and frequency domains, this method matches the computational efficiency of FFT based algorithms without, unlike such algorithms, compromising precision. We apply this approach to data from LIGO's third observing run, directly comparing its performance with that of a previous search. Our results show a consistent 15 percent improvement across nearly the entire frequency range, without additional computational costs. With potential for further refinements, this method already offers a solution capable of maximizing the scientific potential of current and future gravitational-wave observatories.

astro-ph.CO↗

arXiv2024

Real-time gravitational-wave inference for binary neutron stars using machine learning

Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and gravity. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations; however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we present a machine learning framework that performs complete BNS inference in just one second without making any such approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $\sim30\%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.

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arXiv2024

The Impact of Astrophysical Priors on Parameter Inference for GW230529

We investigate the effects of prior selection on the inferred mass and spin parameters of the neutron star-black hole merger GW230529\_181500. Specifically, we explore models motivated by astrophysical considerations, including massive binary and pulsar evolution. We examine mass and spin distributions of neutron stars constrained by radio pulsar observations, alongside black hole spin observations from previous gravitational wave detections. We show that the inferred mass distribution highly depends upon the spin prior. Specifically, under the most restrictive, binary stellar evolution models, we obtain narrower distributions of masses with a black hole mass of $4.3^{+0.1}_{-0.1}\,M_{\odot}$and neutron star mass of $1.3^{+0.03}_{-0.03}\,M_{\odot}$ where, somewhat surprisingly, it is the prior on component spins which has the greatest impact on the inferred mass distributions. Re-weighting using neutron star mass and spin priors from observations of radio pulsars, with black hole spins from observations of gravitational waves, yields the black hole and the neutron star masses to be $3.8^{+0.5}_{-0.6} \,M_\odot$ and $1.4^{+0.2}_{-0.1} \,M_\odot$ respectively. The sequence of compact object formation -- whether the neutron star or the black hole formed first -- cannot be determined at the observed signal-to-noise ratio. However, there is no evidence that the black hole was tidally spun up.

astro-ph.HE↗

arXiv2024

Simulation-based Inference for Gravitational-waves from Intermediate-Mass Binary Black Holes in Real Noise

We present an exploratory investigation into using Simulation-based Inference techniques, specifically Flow-Matching Posterior Estimation, to construct a posterior density estimator trained using real gravitational-wave detector noise. Our prototype estimator is trained on a 9-dimensional space, and for training efficiency outputs posterior probability distributions for the binary black holes chirp mass and mass ratio. We use this prototype estimator to investigate possible effects on parameter estimation for Intermediate-Mass Binary Black Holes, and show statistically significant reduction in measurement bias. Although the results show potential for improved measurements, they also highlight the need for further work.

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arXiv2024

Searching for scalar field dark matter with LIGO

We report on a direct search for scalar field dark matter using data from LIGO's third observing run. We analyse the coupling of size oscillations of the interferometer's beamsplitter and arm test masses that may be caused by scalar field dark matter. Using new efficient search methods to maximise sensitivity for signatures of such oscillations, we set new upper limits for the coupling constants of scalar field dark matter as a function of its mass, which improve upon bounds from previous direct searches by several orders of magnitude in a frequency band from 10 Hz to 180 Hz.

astro-ph.CO↗

arXiv2022

Addressing the challenges of detecting time-overlapping compact binary coalescences

Standard detection and analysis techniques for transient gravitational waves make the assumption that detector data contains, at most, one signal at any time. As detectors improve in sensitivity, this assumption will no longer be valid. In this paper we examine how current search techniques for transient gravitational waves will behave under the presence of more than one signal. We perform searches on data sets containing time-overlapping compact binary coalescences. This includes a modelled, matched filter search (PyCBC), and an unmodelled coherent search, coherent WaveBurst (cWB). Both of these searches are used by the LIGO-Virgo-KAGRA collaboration. We find that both searches are capable of identifying both signals correctly when the signals are dissimilar in merger time, $|\Delta t_c| \geq 1$ second, with PyCBC losing only $1\%$ of signals for overlapping binary black hole mergers. Both pipelines can find signal pairings within the region $|\Delta t_c| < 1$ second. However, clustering routines in the pipelines will cause only one of the two signals to be recovered, as such the efficiency is reduced. Within this region, we find that cWB can identify both signals. We also find that matched filter searches can be modified to provide estimates of the correct parameters for each signal.

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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.

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arXiv2021

Density estimation with Gaussian processes for gravitational-wave posteriors

The properties of black-hole and neutron-star binaries are extracted from gravitational-wave signals using Bayesian inference. This involves evaluating a multi-dimensional posterior probability function with stochastic sampling. The marginal probability density distributions from which the samples are drawn are usually interpolated with kernel density estimators. Since most post-processing analysis within the field is based on these parameter estimation products, interpolation accuracy of the marginals is essential. In this work, we propose a new method combining histograms and Gaussian Processes as an alternative technique to fit arbitrary combinations of samples from the source parameters. This method comes with several advantages such as flexible interpolation of non-Gaussian correlations, Bayesian estimate of uncertainty, and efficient re-sampling with Hamiltonian Monte Carlo.

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arXiv2021

Parameter Estimation Bias From Overlapping Binary Black Hole Events In Second Generation Interferometers

Since the initial detection of Gravitational Waves in 2015, 50 candidate events have been reported by the LIGO-Virgo-KAGRA collaboration. As the current generation of detectors move towards their design sensitivity the rate of these detections will increase. The next generation of detectors are likely to have high enough sensitivities that multiple merging binaries will be visible at the same time. In this paper we show that this is likely to happen before the end of the decade, with the move to the LIGO-Voyager detector. We investigate the situation of overlapping Binary-Black-Hole mergers in these detectors. We find that current parameter estimation techniques are capable of distinguishing the louder of two merging BBH events, without significant bias, when their merger times are not less than $\sim0.1$ seconds apart and when the ratio of the signal-to-noise ratios of the systems is uneven. This region of overlapping parameter space is dependent upon the sky locations of the signals and the relation of those locations to the light travel time between detectors. We also find that, if two signals are highly overlapping, then the recovered set of parameters often show strong evidence of precession. Finally we show that bias can occur even when the signal causing the bias is below the detection threshold.

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arXiv2021

Direct limits for scalar field dark matter from a gravitational-wave detector

The nature of dark matter remains unknown to date; several candidate particles are being considered in a dynamically changing research landscape. Scalar field dark matter is a prominent option that is being explored with precision instruments, such as atomic clocks and optical cavities. Here we report on the first direct search for scalar field dark matter utilising a gravitational-wave detector, which operates beyond the quantum shot-noise limit. We set new upper limits for the coupling constants of scalar field dark matter as a function of its mass, by excluding the presence of signals that would be produced through the direct coupling of this dark matter to the beamsplitter of the GEO$\,$600 interferometer. The new constraints improve upon bounds from previous direct searches by more than six orders of magnitude, and are in some cases more stringent than limits obtained in tests of the equivalence principle by up to four orders of magnitude. Our work demonstrates that scalar field dark matter can be probed or constrained with direct searches using gravitational-wave detectors, and highlights the potential of quantum-enhanced interferometry for dark matter detection.

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arXiv2020

PyROQ: a Python-based Reduced Order Quadrature Building Code for Fast Gravitational Wave Inference

The next generation of gravitational-wave observatories will reach low frequency limits on the orders of a few Hz, thus enabling the detection of gravitational wave signals of very long duration. The run time of standard parameter estimation techniques with these long waveforms can be months or even years, making it impractical with existing Bayesian inference pipelines. Reduced order modeling and reduced order quadrature integration rule have recently been exploited as promising techniques that can greatly reduce parameter estimation computational costs. We describe a Python-based reduced order quadrature building code, PyROQ, which builds the reduced order quadrature data needed to accelerate parameter estimation of gravitational waves. We present the first bases for the IMRPhenomXPHM waveform model of binary-black-hole coalescences, including subdominant harmonic modes and precessing spins effects. Furthermore, the code infrastructure makes it directly applicable to the gravitational wave inference for space-borne detectors such as the Laser Interferometer Space Antenna (LISA).

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arXiv2020

Rapid Parameter Estimation of Gravitational Waves from Binary Neutron Star Coalescence using Focused Reduced Order Quadrature

Rapid parameter estimation of gravitational waves from binary neutron star coalescence, in particular accurate sky localisation in minutes after the initial detection stage, is crucial for the success of multi-messenger observations. One of the techniques to speed up the parameter estimation, which has been applied for the production analysis of the LIGO-Virgo collaboration, is reduced order quadrature (ROQ). While it speeds up parameter estimation significantly, the time required is still on the order of hours. Focusing on the fact that the parameter-estimation follow-up can be tuned with the information available at the detection stage, we improve the ROQ technique and develop a new technique, which we designate focused reduced order quadrature (FROQ). We find that FROQ speeds up the parameter estimation by a factor of $\mathcal{O}(10^3)$ to $\mathcal{O}(10^4)$ and enables providing accurate source properties such as the location of a source in several tens of minutes after detection.

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arXiv2020

PESummary: the code agnostic Parameter Estimation Summary page builder

PESummary is a Python software package for processing and visualising data from any parameter estimation code. The easy to use Python executable scripts and extensive online documentation has resulted in PESummary becoming a key component in the international gravitational-wave analysis toolkit. PESummary has been developed to be more than just a post-processing tool with all outputs fully self-contained. PESummary has become central to making gravitational-wave inference analysis open and easily reproducible.

astro-ph.IM↗

arXiv2019

Parameter Estimation with a spinning multi-mode waveform model: IMRPhenomHM

Gravitational waves from compact binary coalescence sources can be decomposed into spherical-harmonic multipoles, the dominant being the quadrupole ($\ell=2, m=\pm2$) modes. The contribution of sub-dominant modes towards total signal power increases with increasing binary mass ratio and source inclination to the detector. It is well-known that in these cases neglecting higher modes could lead to measurement biases, but these have not yet been quantified with a higher-mode model that includes spin effects. In this study, we use the multi-mode aligned-spin phenomenological waveform model IMRPhenomHM to investigate the effects of including multi-mode content in estimating source parameters and contrast the results with using a quadrupole-only model (IMRPhenomD). We use as sources IMRPhenomHM and hybrid EOB-NR waveforms over a range of mass-ratio and inclination combinations, and recover the parameters with IMRPhenomHM and IMRPhenomD. These allow us to quantify the accuracy of parameter measurements using a multi-mode model, the biases incurred when using a quadrupole-only model to recover full (multi-mode) signals, and the systematic errors in the IMRPhenomHM model. We see that the parameters recovered by multi-mode templates are more precise for all non-zero inclinations as compared to quadrupole templates. For multi-mode injections, IMRPhenomD recovers biased parameters for non-zero inclinations with lower likelihood while IMRPhenomHM recovered parameters are accurate for most cases, and if a bias exists, it can be explained as a combined effect of observational priors and (in the case of hybrid-NR signals) waveform inaccuracies. For cases where IMRPhenomHM recovers biased parameters, the bias is always smaller than the corresponding IMRPhenomD recovery, and we conclude that IMRPhenomHM will be sufficiently accurate to allow unbiased measurements for most GW observations.

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arXiv2019

Measuring the neutron star equation of state with gravitational waves: the first forty binary neutron star mergers

Gravitational waves from binary neutron star coalescences contain rich information about matter at supranuclear densities encoded by the neutron star equation of state. We can measure the equation of state by analyzing the tidal interactions between neutron stars, which is quantified by the tidal deformability. Multiple merger events are required to probe the equation of state over a range of neutron star masses. The more events included in the analysis, the stronger the constraints on the equation of state. In this paper, we build on previous work to explore the constraints that LIGO and Virgo are likely to place on the neutron star equation of state by combining the first forty binary neutron star detections, a milestone we project to be reached during the first year of accumulated design-sensitivity data. We carry out Bayesian inference on a realistic mock dataset of binaries to obtain posterior distributions for neutron star tidal parameters. In order to combine posterior samples from multiple observations, we employ a random forest regressor, which allows us to efficiently interpolate the likelihood distribution. Assuming a merger rate of 1540 Gpc$^{-3}$ yr$^{-1}$ and a LIGO-Virgo detector network operating for one year at the sensitivity of the third-observation run, plus an additional eight months of design sensitivity, we find that the radius of a 1.4 $M_\odot$ neutron star can be constrained to $\sim 10$% at 90% confidence. At the same time, the pressure at twice the nuclear saturation density can be constrained to $\sim 45$ % at 90% confidence. Finally, we add an appendix following publication of the paper in the journal, showing the posterior distribution of the maximum neutron star mass allowed by the equation of state. We find that the maximum mass can be constrained to $\sim 0.3$ $M_\odot$ at 90% confidence.

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