arXiv subjects

Stephen R. Green

Publications and source records attributed to Stephen R. Green.

At least 19 recordsLinked to original sources

arXiv2026

Black hole spectroscopy: from theory to experiment

The "ringdown" radiation emitted by oscillating black holes has great scientific potential. By carefully predicting the frequencies and amplitudes of black hole quasinormal modes and comparing them with gravitational-wave data from compact binary mergers we can advance our understanding of the two-body problem in general relativity, verify the predictions of the theory in the regime of strong and dynamical gravitational fields, and search for physics beyond the Standard Model or new gravitational degrees of freedom. We summarize the state of the art in our understanding of black hole quasinormal modes in general relativity and modified gravity, their excitation, and the modeling of ringdown waveforms. We also review the status of LIGO-Virgo-KAGRA ringdown observations, data analysis techniques, and the bright prospects of the field in the era of LISA and next-generation ground-based gravitational-wave detectors.

gr-qc↗

arXiv2026

Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response at fixed reference time. After sampling, we importance-sample to the true posterior based on the underlying likelihood and prior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of $\sim\!500$. At higher signal-to-noise ratios of $\sim\!1000$, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods. The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The proposed approach allows for straightforward generalizations, including a time-dependent detector response, non-stationary noise artifacts such as gaps and glitches, and low-latency parameter estimations.

astro-ph.HE↗

arXiv2026

Schrödinger perturbation theory for black hole quasinormal modes

Deviations from vacuum general relativity (such as modified theories or the presence of an environment) produce small shifts in black hole quasinormal mode (QNM) spectra. These effects are becoming increasingly relevant for gravitational wave astronomy as observations of ringdown spectra become more precise. The first-order frequency shift (in a small dimensionless coupling parameter) is now well understood, but no systematic framework exists to compute higher order corrections. The major obstacle is that QNMs do not form a complete basis due to the non-self-adjointness of the system. Nevertheless, it was recently shown that QNMs are orthogonal with respect to an appropriate bilinear form. In this work, we use the bilinear form to systematically lift Schrödinger perturbation theory to the black hole setting. We obtain a formula for quasinormal frequency shifts to any order, in terms of lower order mode shifts. We also provide a spectral decomposition of the first-order mode shift, which involves projections onto unperturbed QNMs along with continuous-spectrum contributions -- making incompleteness explicit. We illustrate the framework on slowly-spinning Kerr and Pöschl-Teller examples, where we find that the QNM sum itself diverges.

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arXiv2026

Eccentricity constraints disfavor single-single capture in nuclear star clusters as the origin of all LIGO-Virgo-KAGRA binary black holes

Multiple formation pathways have been proposed for the origin of binary black holes (BBHs). These include isolated binary evolution and dynamical assembly in dense stellar environments such as nuclear or globular star clusters. Yet, the fraction of BBHs originating from each channel remains uncertain. One way to constrain this fraction is by investigating the orbital eccentricities of the BH coalescences detected by the LIGO-Virgo-KAGRA (LVK) Collaboration. We analyze 84 BBHs from the first part of the fourth LVK observing run (O4a) using a multipolar, eccentric, aligned-spin effective-one-body waveform model. We perform parameter inference with neural posterior estimation and nested sampling. After incorporating astrophysical prior odds and comparing to the quasicircular precessing-spin hypothesis, we find that no candidates reach a high enough significance to claim a confident detection of eccentricity. We use these upper limits to explore a model, in which all O4a BBHs originate from single-single gravitational wave (GW) captures. We perform hierarchical inference on the velocity dispersion of the host environment of the BBHs and find $σ$ < 19.7 km/s (95% credible upper bound). This disfavors single-single capture in nuclear star clusters (approximately 20-200 km/s) as the dominant source of all observed BBH mergers. Our analysis also jointly infers the mass, spin and redshift distributions and takes into account selection effects due to using quasi-circular templates for BBH detection. Our results place improved constraints on the number of eccentric BBHs and highlight the importance of eccentricity measurements in disentangling compact-binary formation channels in current and future GW detectors.

astro-ph.HE↗

arXiv2026

Flexible Gravitational-Wave Parameter Estimation with Transformers

Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge. Deep learning provides a powerful alternative to traditional inference, but existing neural models typically lack the flexibility to handle variations in data analysis settings. Such variations accommodate imperfect observations or are required for specialized tests, and could include changes in detector configurations, overall frequency ranges, or localized cuts. We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time. Applied to parameter estimation, we demonstrate that a single flexible model, called Dingo-T1, can (i) analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run under a wide range of analysis configurations, (ii) enable systematic studies of how detector and frequency configurations impact inferred posteriors, and (iii) perform inspiral-merger-ringdown consistency tests probing general relativity. Dingo-T1 also improves median sample efficiency on real events from a baseline of 1.4% to 4.2%. Our approach thus demonstrates flexible and scalable inference with a principled framework for handling missing or incomplete data, key capabilities for current and next-generation observatories.

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arXiv2026

Accelerated inference of microlensed gravitational waves with machine learning

Gravitational waves (GWs) within the LIGO-Virgo-KAGRA sensitivity band can be microlensed by stellar and intermediate-mass black holes, producing a frequency-dependent modulation of the signal amplitude. Microlensing analyses, however, are costly due to the increased dimensionality of the parameter space and waveform computation time. As a proof of concept, we show that the deep-learning-based framework Deep Inference for Gravitational-Wave Observations (DINGO), which employs a simulation-based inference approach to estimate posterior distributions, can perform efficient parameter inference for GW microlensing by an isolated point-mass lens. Using simulated microlensed GW signals, we train a lensed-DINGO network and compare its performance with traditional Bayesian parameter estimation carried out with Bilby. Our framework can be used to rapidly identify microlensed events in large GW catalogs. When the lensed-DINGO network is combined with importance sampling, we find that although sample efficiencies are somewhat reduced compared to the unlensed-DINGO network, owing to the richer structure of microlensed signals, it still achieves $\mathcal{O}(10\times)$ speed-up relative to Bilby. We further show that this framework is useful to efficiently estimate the background Bayes-factor distribution, which is crucial for assessing the significance of candidate lensed events. However, for foreground (lensed) events, the sampling efficiency can sometimes drop when analysed with the unlensed-DINGO network, providing a diagnostic indicator of out-of-distribution data. Our approach can be straightforwardly generalised to more complex and realistic lens models, enabling detailed studies of microlensed GWs.

astro-ph.CO↗

arXiv2026

End-to-End Population Inference from Gravitational-Wave Strain using Transformers

The population of compact binaries encodes information about their astrophysical origins and the expansion of the universe. Hierarchical Bayesian methods infer these properties by combining single-event posteriors. As catalogs grow, however, this approach becomes computationally expensive and is subject to increasing Monte Carlo uncertainty. We introduce Dingo-Pop, a simulation-based framework that infers population posteriors directly from gravitational-wave strain data. The data for each event are embedded into low-dimensional tokens and combined using a transformer trained on simulated catalogs subject to selection effects. This enables (i) population inference without per-event Monte Carlo sampling noise, (ii) amortization across variable catalog sizes using a single network, and (iii) end-to-end inference in about one second. We train a network for catalog sizes of 25 to 1000 events, and obtain well-calibrated posteriors consistent with traditional methods. By avoiding per-event analyses that can take hours to days, Dingo-Pop enables new classes of large-scale injection studies; as an application, we examine how spectral-siren Hubble constant uncertainties change with catalog size.

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arXiv2026

Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA

Binary black holes (BBHs) in eccentric orbits produce distinct modulations in gravitational waves (GWs); measuring orbital eccentricity provides evidence for dynamical binary formation channels. We analyze 57 GW events from the LIGO-Virgo-KAGRA (LVK) O1-O3 observing runs using a multipolar aligned-spin inspiral-merger-ringdown waveform with two eccentric parameters: eccentricity and relativistic anomaly (assuming a quasi-circular merger-ringdown), made computationally feasible by the machine-learning code \texttt{DINGO}, which accelerates inference by 2-3 orders of magnitude. First, with a uniform eccentricity prior, eccentric vs. quasi-circular aligned-spin $\log_{10}$ Bayes factors are 1.84-4.75 (depending on glitch mitigation) for GW200129, 3.0 for GW190701 and 1.77 for GW200208_22. We infer $e_{\text{gw, 10Hz}}$ $(e_{\text{gw, 20Hz}})$ to be $0.27_{-0.12}^{+0.10}$ ($0.16_{-0.05}^{+0.04}$) to $0.17_{-0.13}^{+0.14}$ ($0.1_{-0.04}^{+0.05}$) for GW200129, $0.54_{-0.30}^{+0.12}$ ($0.31_{-0.13}^{+0.12}$) for GW190701 and $0.39_{-0.23}^{+0.23}$ ($0.21_{-0.08}^{+0.08}$) for GW200208_22. Second, eccentric aligned-spin vs. quasi-circular precessing-spin $\log_{10}$ Bayes factors are 1.43-4.92 for GW200129, 2.61 for GW190701 and 1.23 for GW200208_22. Third, GW190521 shows no evidence for eccentricity ($\log_{10}$ Bayes factor 0.04). Fourth, neglecting spin-precession with an astrophysically-motivated prior on the eccentric BBH rate, the probability of one of the 57 events being eccentric exceeds 99.5\% or $(100-8.4\times10^{-4})$\% (depending on glitch mitigation). Fifth, we study parameter estimation impacts of neglecting eccentricity in quasi-circular models or higher modes in eccentric models. These results underscore the inclusion of eccentric parameters in BBH characterization for upcoming LVK runs and future ground- and space-based detectors probing more diverse BBH populations.

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arXiv2026

Waveform Modelling for the Laser Interferometer Space Antenna

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome.

gr-qc↗

arXiv2026

Kahler decoupling for Kerr perturbations

The Euclidean Kerr metric is conformal, in two distinct ways, to a Kahler metric, with conformal factors determined by the repeated eigenvalue of the two chiral halves of the Weyl curvature. A Lorentzian analogue holds, where the conformally related metric is complex but retains key features of Kahler geometry. We show that this hidden Kahler structure provides a geometric explanation for the existence of decoupled equations for curvature scalars, such as the Teukolsky equations. The essential mechanism is that, on a Kahler background, self-dual 2-forms are parallel with respect to a natural covariant derivative, so differential operators acting on them preserve their decomposition and do not mix components. In this way, decoupling is seen to be a direct consequence of Kahler geometry. We make this mechanism explicit in two ways. First, we show that the spin-k Teukolsky operator can be obtained from a Laplace-type operator associated with the Kahler metric by a similarity transformation. Second, for electromagnetic perturbations, we use the conformal invariance of Maxwell's equations delta F = 0 to show that they imply d delta F = 0, where delta is the co-differential of the Kahler metric. This operator automatically decouples, and the resulting equations for the extremal components coincide with the spin-one Teukolsky equations.

gr-qc↗

arXiv2025

Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope

The Einstein Telescope (ET), along with other third-generation gravitational wave (GW) detectors, will be a key instrument for detecting GWs in the coming decades. However, analyzing the data and estimating source parameters will be challenging, especially given the large number of expected detections-on the order of $10^5$ per year-which makes current methods based on stochastic sampling impractical. In this work, we use Dingo-IS to perform neural posterior estimation (NPE) of high-redshift events detectable with ET in its triangular configuration. NPE is a likelihood-free inference technique that leverages normalizing flows to approximate posterior distributions. After training, inference is fast, requiring only a few minutes per source, and accurate, as corrected through importance sampling and validated against standard Bayesian inference methods. To confirm previous findings on the ability to estimate parameters for high-redshift sources with ET, we compare NPE results with predictions from the Fisher information matrix (FIM) approximation. We find that NPE correctly recovers the eight degenerate sky modes induced by the triangular detector geometry, missed by the FIM analysis, resulting in an underestimation of sky localization uncertainties for most sources. FIM also overestimates the uncertainty in luminosity distance by a factor of $\sim 3$ on average when the injected luminosity distance is $d^{\mathrm{inj}}_{\mathrm{L}} > 10^5~$Mpc, further confirming that ET will be particularly well suited for studying the early Universe.

astro-ph.HE↗

arXiv2025

GW200208_222617 as an eccentric black-hole binary merger: properties and astrophysical implications

Detecting orbital eccentricity in a stellar-mass black-hole merger would point to a non-isolated formation channel. Eccentric binaries can form in dense stellar environments such as globular clusters or active galactic nuclei, or from triple stellar systems in the Galactic field. However, confidently measuring eccentricity is challenging -- short signals from high-mass eccentric mergers can mimic spin-induced precession, making the two effects hard to disentangle. This degeneracy weakens considerably for longer-duration signals. Here, GW200208_222617 provides a rare opportunity. Originating from a relatively low-mass binary with source-frame chirp mass $\sim20$ M$_\odot$, its gravitational-wave signal spanned $\sim14$ orbital cycles in band, with no indication of data quality issues. Previous analyses for quasi-circular binaries found no evidence for spin precession, and multiple subsequent studies found the data to favour an eccentric merger despite notable technical differences. All in all, we believe GW200208_222617 is the black-hole merger event from GWTC-3 with the least ambiguous detection of eccentricity. We present a critical discussion of properties and astrophysical interpretation of GW200208_222617 as an eccentric black-hole merger using models of field triples, globular clusters, and active galactic nuclei. We find that if GW200208_222617 was indeed eccentric, its origin is consistent with a field triple or globular cluster. Formation in the inner regions of an active galactic nucleus is disfavoured. The outer regions of such a disk remain a viable origin for GW200208_222617; we demonstrate how future detections of eccentric mergers formed in such environments could be powerful tools for constraining the disk geometry.

astro-ph.HE↗

arXiv2024

Extremal Black Hole Weather

We consider weakly non-linear gravitational perturbations of a near-extremal Kerr black hole governed by the second order vacuum Einstein equation. Using the GHZ formalism [Green et al., Class. Quant. Grav. 7(7):075001, 2020], these are parameterized by a Hertz potential. We make an ansatz for the Hertz potential as a series of zero-damped quasinormal modes with time-dependent amplitudes, and derive a non-linear dynamical system for them. We find that our dynamical system has a time-independent solution within the near horizon scaling limit. This equilibrium solution is supported on axisymmetric modes, with amplitudes scaling as $c_\ell \sim C^{\rm low} 2^{-\ell/2} \ell^{-\frac{7}{2}}$ for large polar angular momentum mode number $\ell$, where $C^{\rm low}$ is a cumulative amplitude of the low $\ell$ modes. We interpret our result as evidence that the dynamical evolution will approach, for a parametrically long time as extremality is approached, a distribution of mode amplitudes dyadically exponentially suppressed in $\ell$, hence as the endpoint of an inverse cascade. It is reminiscent of weather-like phenomena in certain models of atmospheric dynamics of rotating bodies. During the timescale considered, the decay of the QNMs themselves plays no role given their parametrically long half-life. Hence, our result is due entirely to weakly non-linear effects.

gr-qc↗

arXiv2024

Incorporating waveform calibration error in gravitational-wave modeling and inference for SEOBNRv4

As gravitational wave (GW) detector networks continue to improve in sensitivity, the demand on the accuracy of waveform models which predict the GW signals from compact binary coalescences is becoming more stringent. At high signal-to-noise ratios (SNRs) discrepancies between waveform models and the true solutions of Einstein's equations can introduce significant systematic biases in parameter estimation (PE). These biases affect the inferred astrophysical properties, including matter effects, and can also lead to erroneous claims of deviations from general relativity, impacting the interpretation of astrophysical populations and cosmological parameters. While efforts to address these biases have focused on developing more precise models, we explore an alternative strategy to account for uncertainties in waveform models, particularly from calibrating an effective-one-body (EOB) model against numerical relativity (NR) data. We introduce an efficient method for modeling and marginalizing over waveform uncertainty in the SEOBNRv4 model, which captures the dominant $(2,2)$ mode for non-precessing quasi-circular binary black holes (BBHs). Our approach uses Gaussian process regression (GPR) to model amplitude and phase deviations in the Fourier domain. This method mitigates systematic biases in PE and increases posterior variance by incorporating a broader distribution of waveforms, consistent with previous findings. This study emphasizes the importance of incorporating waveform uncertainties in GW data analysis and presents a novel, practical framework to include these uncertainties in Bayesian PE for EOB models, with broad applicability.

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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

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.

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arXiv2024

Conserved currents for Kerr and orthogonality of quasinormal modes

We introduce a bilinear form for Weyl scalar perturbations of Kerr. The form is symmetric and conserved, and we show that, when combined with a suitable renormalization prescription involving complex r integration contours, quasinormal modes are orthogonal in the bilinear form for different (l, m, n). These properties are apparently not evident consequences of standard properties for the radial and angular solutions to the decoupled Teukolsky relations and rely on the Petrov type D character of Kerr and its t-$ϕ$ reflection isometry. We show that quasinormal mode excitation coefficients are given precisely by the projection with respect to our bilinear form. These properties can make our bilinear form useful to set up a framework for nonlinear quasinormal mode coupling in Kerr. We also provide a general discussion on conserved local currents and their associated local symmetry operators for metric and Weyl perturbations, identifying a collection containing an increasing number of derivatives.

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arXiv2024

Relativistic perturbation theory for black-hole boson clouds

We develop a relativistic perturbation theory for scalar clouds around rotating black holes. We first introduce a relativistic product and corresponding orthogonality relation between modes, extending a recent result for gravitational perturbations. We then derive the analog of time-dependent perturbation theory in quantum mechanics, and apply it to calculate self-gravitational frequency shifts. This approach supersedes the non-relativistic "gravitational atom" approximation, brings close agreement with numerical relativity, and has practical applications for gravitational-wave astronomy.

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