arXiv subjects

Jonathan Gair

Publications and source records attributed to Jonathan Gair.

At least 37 records · Page 2Linked to original sources

arXiv2025

Clustering effects on the Dark Siren determination of $H_0$: A simulation study

Gravitational waves (GWs) offer an alternative way to measure the Hubble parameter. The optimal technique, the ``bright siren'' approach, requires the identification of an electromagnetic counterpart. However, a significant fraction of gravitational waves signals will not have counterparts. Such events can still constrain the Hubble parameter $H_0$ via statistical methods, exploiting galaxy information from the GWs sky localisation volume. In this work, we investigate the power of this method using high-resolution, cosmological simulations that include realistic clustering. We find that clustering leads to increased convergence of the $H_0$ posteriors, with clear recovery of the input value as early as $N_{\rm gw}=40$ events, compared to uniform catalogues, where the posterior remains largely unconstrained, even with $N_{\rm gw}=100$ events. In addition, we quantify the role of catalogue incompleteness. We show that catalogues with completeness levels as low as $f=25\%$ can be competitive with fully complete catalogues, confirming the impact of clustering. Completeness levels of $f=50\%$ perform statistically similar to complete catalogues with as few as $N_{\rm gw}=40$ events. This indicates the need to focus on improving gravitational waves detection capabilities, rather than obtaining more complete galaxy catalogues. Finally, we investigate additional properties of the method by taking into consideration physical weights, different observational errors, potential biases from the $H_0$ priors, a variety of detectors' horizon distances, and different methods of catalogue completion and statistical analysis.

astro-ph.CO↗

arXiv2025

Systematic Biases in Estimating the Properties of Black Holes Due to Inaccurate Gravitational-Wave Models

Gravitational-wave (GW) observations of binary black-hole (BBH) coalescences are expected to address outstanding questions in astrophysics, cosmology, and fundamental physics. Realizing the full discovery potential of upcoming LIGO-Virgo-KAGRA (LVK) observing runs and new ground-based facilities hinges on accurate waveform models. Using linear-signal approximation methods and Bayesian analysis, we start to assess our readiness for what lies ahead using two state-of-the-art quasi-circular, spin-precessing models: \texttt{SEOBNRv5PHM} and \texttt{IMRPhenomXPHM}. We ascertain that current waveforms can accurately recover the distribution of masses in the LVK astrophysical population, but not spins. We find that systematic biases increase with detector-frame total mass, binary asymmetry, and spin-precession, with most such binaries incurring parameter biases, extending up to redshifts $\sim3$ in future detectors. Furthermore, we examine three ``golden'' events characterized by large mass ratios, significant spin magnitudes, and high precession, evaluating how systematic biases may affect their scientific outcomes. Our findings reveal that current waveforms fail to enable the unbiased measurement of the Hubble-Lemaître parameter from loud signals, even for current detectors. Moreover, highly asymmetric systems within the lower BH mass-gap exhibit biased measurements of the secondary-companion mass, which impacts the physics of both neutron stars and formation channels. Similarly, we deduce that the primary mass of massive binaries ($ > 60 M_\odot$) will also be biased, affecting supernova physics. Future progress in analytical calculations and numerical-relativity simulations, crucial for calibrating the models, must target regions of the parameter space with significant biases to develop more accurate models. Only then can precision GW astronomy fulfill the promise it holds.

gr-qc↗

arXiv2025

A flexible, GPU-accelerated approach for the joint characterization of LISA instrumental noise and Stochastic Gravitational Wave Backgrounds

LISA data analysis represents one of the most challenging tasks ahead for the future of gravitational-wave (GW) astronomy. Characterizing the instrument's noise properties while fitting for all the other detectable sources is a key requirement of any robust inference pipeline. Noise estimation will also play a crucial role in searches and parameter estimation of cosmological and astrophysical stochastic signals. Previous studies have tackled this topic by assuming perfect knowledge of the spectral shape of the instrumental noise and of different possible types of GW Stochastic Backgrounds (SGWBs), usually resorting to parametrized templates. Recently, various works that employ template-agnostic methods have been presented. In this work, we take an additional step further, introducing flexible spectral shapes in both the instrumental noise and the stochastic signals. We account for the lack of knowledge of the exact shape of the individual contributions to the overall power spectral density by using splines to represent arbitrary perturbations of the noise and signal spectral densities. We implement a data-driven Reversible Jump MCMC algorithm to fit different components simultaneously and to infer the level of flexibility required under different scenarios. We test this approach on simulated LISA data produced under different assumptions. We investigate the impact of this increased flexibility on the reconstruction of both the injected signal and the noise level, and we discuss the prospects for claiming a successful SGWB detection.

gr-qc↗

arXiv2025

Is your stochastic signal really detectable?

Separating a stochastic gravitational wave background (SGWB) from noise is a challenging statistical task. One approach to establishing a detection criterion for the SGWB is using Bayesian evidence. If the evidence ratio (Bayes factor) between models with and without the signal exceeds a certain threshold, the signal is considered detected. We present a formalism to compute the averaged Bayes factor, incorporating instrumental-noise and SGWB uncertainties. As an example, we consider the case of power-law-shaped SGWB in LISA and generate the corresponding Bayesian sensitivity curve. Unlike existing methods in the literature, which typically neglect uncertainties in both the signal and noise, our approach provides a reliable and realistic alternative. This flexible framework opens avenues for more robust stochastic gravitational wave background detection across gravitational-wave experiments.

astro-ph.IM↗

arXiv2025

A pipeline for searching and fitting instrumental glitches in LISA data

Instrumental artefacts, such as glitches, can significantly compromise the scientific output of LISA. Our methodology employs advanced Bayesian techniques, including Reversible Jump Markov Chain Monte Carlo and parallel tempering to find and characterize glitches and astrophysical signals. The robustness of the pipeline is demonstrated through its ability to simultaneously handle diverse glitch morphologies and it is validated with a 'Spritz'-type data set from the LISA Data Challenge. Our approach enables accurate inference on Massive Black Hole Binaries, while simultaneously characterizing both instrumental artefacts and noise. These results present a significant development in strategies for differentiating between instrumental noise and astrophysical signals, which will ultimately improve the accuracy and reliability of source population analyses with LISA.

gr-qc↗

arXiv2025

The fault in our sirens: Hierarchical diagnosis of waveform systematics in Hubble-Lemaître constant measurements

Cosmological inference using a population of binary black-hole mergers, combined with a galaxy catalog, presents an exciting opportunity for precision cosmology with the possibility of resolving the Hubble tension. However, the accuracy of these measurements heavily relies on the quality of the model used to infer the binary parameters, including the model of the gravitational-wave signal. We use state-of-the-art waveform models to explore the impact of inaccurate modeling in measuring the Hubble-Lemaître constant for the upcoming and future ground-based gravitational-wave observatories. We diagnose the presence of inaccuracies within a hierarchical population-analysis framework, without a priori knowing the true value of the parameter, by assessing the consistency of the distribution of individual posteriors in relation to their measurement errors. Our findings indicate that even a small high-mass, spin-precessing subpopulation -- comprising as little as 5\% of the population generating the events observed by the LIGO-Virgo-KAGRA Collaboration so far -- can result in an unreliable measurement of the Hubble-Lemaître constant in the upcoming observing runs of these detectors, with even more pronounced effects expected in future facilities on the ground.

gr-qc↗

arXiv2025

Bias-Corrected Importance Sampling for Inferring Beyond-Vacuum-GR Effects in Gravitational-Wave Sources

The upcoming gravitational wave (GW) observatory LISA will measure the parameters of sources like extreme-mass-ratio inspirals (EMRIs) to exquisite precision. These measurements will also be sensitive to perturbations to the vacuum, GR-consistent evolution of sources, which might be caused by astrophysical environments or deviations from general relativity (GR). Previous studies have shown such ``beyond-vacuum-GR'' perturbations to potentially induce severe biases ($\gtrsim 10σ$) on recovered parameters under the ``null'' vacuum-GR hypothesis. While Bayesian inference can be performed under the null hypothesis using Markov Chain Monte Carlo (MCMC) samplers, it is computationally infeasible to repeat for more than a modest subset of all possible beyond-vacuum-GR hypotheses. We introduce bias-corrected importance sampling, a generic inference technique for nested models that is informed by the null hypothesis posteriors and the linear signal approximation to correct any induced inference biases. For a typical EMRI source that is significantly influenced by its environment but has been inferred only under the null hypothesis, the proposed method efficiently recovers the injected (unbiased) source parameters and the true posterior at a fraction of the expense of redoing MCMC inference under the full hypothesis. In future GW data analysis using the output of the proposed LISA global-fit pipeline, such methods may be necessary for the feasible and systematic inference of beyond-vacuum-GR effects.

gr-qc↗

arXiv2025

Reconstructing parametric gravitational-wave population fits from non-parametric results without refitting the data

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation.

astro-ph.HE↗

arXiv2025

Data-driven approach for extracting tidal information from neutron star binary mergers observed with the Einstein Telescope

The recent breakthroughs regarding the detection of compact binary mergers via gravitational waves opened up a new window to the Universe. Gravitational-wave models have been essential to this success since they are necessary to infer the properties of the compact binary system from the observational data. Next-generation detectors, such as the Einstein Telescope, will allow for more observations of binary neutron star mergers with higher precision, making accurate waveform models crucial in describing these systems. In this article, we propose a novel approach for constructing phenomenological waveform models informed by observational data. Using mock data representing a one-year operation of the Einstein Telescope as our baseline, we demonstrate how the results improve as more events are included in the calibration. This method offers a new and complementary approach for developing sophisticated gravitational-wave models compared to classical techniques that employ analytical computations and numerical-relativity simulations. Improved waveform models will then yield more accurate parameter estimation.

gr-qc↗

arXiv2025

Impact of relativistic waveforms in LISA's science objectives with extreme-mass-ratio inspirals

Extreme-Mass-Ratio Inspirals (EMRIs) are one of the key targets for future space-based gravitational wave detectors, such as LISA. The scientific potential of these sources can only be fully realized with fast and accurate waveform models. In this work, we extend the \textsc{FastEMRIWaveform} (\texttt{FEW}) framework by providing fully relativistic waveforms at adiabatic order for circular, equatorial orbits in Kerr spacetime, for mass ratios up to $10^{-3}$. We investigate the significance of including relativistic corrections in the waveform for both vacuum and non-vacuum environments. Specifically, we develop relativistic non-vacuum EMRI waveforms including two different environmental effects in the EMRI waveforms: power-law migration torques, and superradiance scalar clouds. For EMRIs in vacuum, we find that non-relativistic waveforms incorrectly estimate the predicted source's horizon redshift by approximately $35\%$ error. Our analysis shows that incorporating relativistic corrections enhances constraints on accretion disks, modeled through power-law torques, and improves the constraints on disk parameter estimates (error $\simeq 8\%$), representing a significant improvement over previous estimates. Additionally, we assess the evidence for models in a scenario where ignoring the accretion disk biases the parameter estimation (PE), reporting a $\log_{10}$ Bayes factor of $1.1$ in favor of the accretion disk model. In a fully relativistic setup, we also estimate the parameters of superradiant scalar clouds with relative errors $\simeq 0.3\%$ for the scalar cloud's mass. These results demonstrate that incorporating relativistic effects is essential for LISA science objectives with EMRIs.

gr-qc↗

arXiv2025

Constraining accretion physics with gravitational waves from eccentric extreme-mass-ratio inspirals

We study the evolution of eccentric, equatorial extreme-mass-ratio inspirals (EMRIs) immersed in the accretion disks of active galactic nuclei. We find that single gravitational-wave observations from these systems could provide measurements with ~ 10 % relative precision of, simultaneously, the disk viscosity and mass accretion rate of the central supermassive black hole. This is possible when the EMRI transitions, within the observation time, from supersonic to subsonic motion relative to the disk gas, for eccentricities e > ~ 0.025-0.1. The estimate of the accretion rate would assist in the identification of the EMRI's host galaxy, or the observation of a direct electromagnetic counterpart, improving the chances of using these sources as cosmological sirens. Our work highlights the rich phenomenology of binary evolution in astrophysical environments and the need to improve the modelling and analysis of these systems for future gravitational-wave astronomy.

gr-qc↗

arXiv2024

Systematics in tests of general relativity using LISA massive black hole binaries

Our current understanding is that an environment - mainly consisting of gas or stars - is required to bring massive black hole binaries (MBHBs) with total redshifted mass $M_z\sim[10^{4},10^7]~{\rm M}_\odot$ to the LISA band from parsec separation. Even in the gravitational wave (GW) dominated final inspiral, realistic environments can non-negligibly speed up or slow down the binary evolution, or leave residual, measurable eccentricity in the LISA band. Despite this fact, most of the literature does not consider environmental effects or orbital eccentricity in modelling GWs from near-equal mass MBHBs. Considering either a circular MBHB embedded in a circumbinary disc or a vacuum eccentric binary, we explore if ignoring either secular gas effects (migration and accretion) or eccentric corrections to the GW waveform can mimic a failure of General Relativity (GR). We use inspiral-only aligned-spin 3.5 post-Newtonian waveforms, a complete LISA response model, and Bayesian inference to perform a parameterized test of GR. For a four-year LISA observation of an MBHB with $M_z=10^{5}~{\rm M}_\odot$, primary-to-secondary mass ratio $q=8$, and component BHs' dimensionless spins $χ_{1,2}=0.9$ at redshift $z=1$, even a moderate gas-disc imprint (Eddington ratio ${\rm f}_{\rm Edd}\sim0.1$) or low initial eccentricity ($e_0\sim10^{-2.5}$) causes a false violation of GR in several PN orders. However, correctly modelling either effect can mitigate systematics while avoiding significant biases in vacuum circular systems. The adoption of LISA makes it urgent to consider gas imprints and eccentricity in waveform models to ensure accurate inference for MBHBs.

astro-ph.GA↗

arXiv2024

Impact of gravitational waveform model systematics on the measurement of the Hubble constant

Matching gravitational-wave observations of binary neutron stars with theoretical model predictions reveals important information about the sources, such as the masses and the distance to the stars. The latter can be used to determine the Hubble constant, the rate at which the Universe expands. One general problem of all astrophysical measurements is that theoretical models only approximate the real underlying physics, which can lead to systematic uncertainties introducing biases. However, the extent of this bias for the distance measurement due to uncertainties of gravitational waveform models is unknown. In this study, we analyze a synthetic population of 38 binary neutron star sources measured with Advanced LIGO and Advanced Virgo at design sensitivity. We employ a set of four different waveform models and estimate model-dependent systematic biases on the extraction of the Hubble constant using the bright siren method. Our results indicate that systematic biases are below statistical uncertainties for the current generation of gravitational-wave detectors.

gr-qc↗

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.

gr-qc↗

arXiv2024

Measuring eccentricity and gas-induced perturbation from gravitational waves of LISA massive black hole binaries

We assess the possibility of detecting both eccentricity and gas effects (migration and accretion) in the gravitational wave (GW) signal from LISA massive black hole binaries (MBHBs) at redshift $z=1$. Gas induces a phase correction to the GW signal with an effective amplitude ($C_{\rm g}$) and a semi-major axis dependence (assumed to follow a power-law with slope $n_{\rm g}$). We use a complete model of the LISA response, and employ a gas-corrected post-Newtonian in-spiral-only waveform model TaylorF2Ecc By using the Fisher formalism and Bayesian inference, we constrain $C_{\rm g}$ together with the initial eccentricity $e_0$, the total redshifted mass $M_z$, the primary-to-secondary mass ratio $q$, the dimensionless spins $χ_{1,2}$ of both component BHs, and the time of coalescence $t_c$. We find that simultaneously constraining $C_{\rm g}$ and $e_0$ leads to worse constraints on both parameters with respect to when considered individually. For a standard thin viscous accretion disc around $M_z=10^5~{\rm M}_\odot$, $q=8$, $χ_{1,2}=0.9$, and $t_c=4$ years MBHB, we can confidently measure (with a relative error of $<50 $ per cent) an Eddington ratio ${\rm f}_{\rm Edd}\sim0.1$ for a circular binary and ${\rm f}_{\rm Edd}\sim1$ for an eccentric system assuming ${O}(10)$ stronger gas torque near-merger than at the currently explored much-wider binary separations. The minimum measurable eccentricity is $e_0\gtrsim10^{-2.75}$ in vacuum and $e_0\gtrsim10^{-2}$ in gas. A weak environmental perturbation (${\rm f}_{\rm Edd}\lesssim1$) to a circular binary can be mimicked by an orbital eccentricity during in-spiral, implying that an electromagnetic counterpart would be required to confirm the presence of an accretion disc.

astro-ph.GA↗

arXiv2024

Gravitational wave populations and cosmology with neural posterior estimation

We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual source observations. This approach provides complete freedom in event representation, automatic inclusion of selection effects, and (in contrast to likelihood estimation) without the need for stochastic samplers to obtain posterior samples. Since the number of events may be unknown when the network is trained, we split into sub-population analyses that we later recombine; this allows for fast sequential analyses as additional events are observed. We demonstrate our method on a toy problem of dark siren cosmology, and show that inference takes just a few minutes and scales to $\sim 600$ events before performance degrades. We argue that neural posterior estimation therefore represents a promising avenue for population inference with large numbers of events.

gr-qc↗

arXiv2024

LISA Definition Study Report

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to distant massive black-hole mergers and the expansion of the Universe. This definition study report, or Red Book, presents a summary of the very large body of work that has been undertaken on the LISA mission over the LISA definition phase.

astro-ph.CO↗

arXiv2023

Bayesian inference of binary black holes with inspiral-merger-ringdown waveforms using two eccentric parameters

Orbital eccentricity is a crucial physical effect to unveil the origin of compact-object binaries detected by ground- and spaced-based gravitational-wave (GW) observatories. Here, we perform for the first time a Bayesian inference study of inspiral-merger-ringdown eccentric waveforms for binary black holes with non-precessing spins using two (instead of one) eccentric parameters: eccentricity and relativistic anomaly. We employ for our study the multipolar effective-one-body (EOB) waveform model SEOBNRv4EHM, and use initial conditions such that the eccentric parameters are specified at an orbit-averaged frequency. We show that this new parametrization of the initial conditions leads to a more efficient sampling of the parameter space. We also assess the impact of the relativistic-anomaly parameter by performing mock-signal injections, and we show that neglecting such a parameter can lead to significant biases in several binary parameters. We validate our model with mock-signal injections based on numerical-relativity waveforms, and we demonstrate the ability of the model to accurately recover the injected parameters. Finally, using standard stochastic samplers employed by the LIGO-Virgo-KAGRA Collaboration, we analyze a set of real GW signals observed by the LIGO-Virgo detectors during the first and third runs. We do not find clear evidence of eccentricity in the signals analyzed, more specifically we measure $e^{\text{GW150914}}_{\text{gw, 10Hz}}= 0.08^{+0.09}_{-0.06}$, $e^{\text{GW151226}}_{\text{gw, 20Hz}}= {0.04}^{+0.05}_{-0.04} $, and $e^{\text{GW190521}}_{\text{gw, 5.5Hz}}= 0.15^{+0.12}_{-0.12}$.

gr-qc↗