arXiv subjects

Ollie Burke

Publications and source records attributed to Ollie Burke.

18 recordsLinked to original sources

arXiv2026

Quantifying the Scientific Potential of Intermediate and Extreme Mass Ratio Inspirals with the Laser Interferometer Space Antenna

The Laser Interferometer Space Antenna (LISA) will enable precision studies of Extreme and Intermediate Mass Ratio Inspirals (EMRIs/IMRIs), providing unique probes of astrophysical environments of galactic nuclei and strong-field gravity. Using a fully relativistic pipeline across primary masses $m_1 \in [5\times10^4, 10^7]\,M_\odot$ and secondary masses $m_2 \in [1, 10^4]\,M_\odot$, we map instrumental performance directly to detection horizons and parameter measurement precision. EMRIs with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ are the most sensitive to instrument degradation, with redshift horizons at $z \sim 0.01$, while IMRIs are the least sensitive to degradation and reach redshifts $z \sim 1-3$. All prograde systems considered achieve sub-percent spin precision within three months of observation. The full 4.5-year mission increases the horizon of systems with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ by a factor of $\sim 4$ and improves sky localization by one to two orders of magnitude reaching $ < 10\,\mathrm{deg}^2$. IMRI detection is robust against degradation, but their parameter estimation is more vulnerable due to fewer cycles in band. With the full baseline, EMRI observations constrain scalar dipole emission and Kerr quadrupole deviations below ground-based bounds by one to two orders of magnitude. We release the accompanying software and an interactive website to enable the community to rapidly quantify the scientific potential of EMRIs and IMRIs.

astro-ph.IM↗

arXiv2026

The significance of first post-adiabatic contributions for scalar charge measurements with intermediate and extreme mass ratio inspirals

We present the first self-force-based beyond-GR waveform model incorporating post-adiabatic orbital evolution for intermediate- and extreme-mass-ratio inspirals in theories of gravity with additional scalar fields. Focusing on quasi-circular inspirals into a non-spinning primary, we combine a first post-adiabatic (1PA) gravitational sector with leading-order scalar field effects and use Bayesian injection-recovery studies to assess the impact of waveform systematics on the inference of scalar charges with LISA. We find that neglecting 1PA effects in the gravitational sector can bias the inference of intrinsic binary parameters, while scalar-charge measurements remain robust across a wide range of mass ratios. In contrast, analysing signals from binaries in which the secondary carries a scalar charge using pure-GR templates leads to significant biases and underestimated uncertainties due to unmodelled correlations between the scalar charge and the binary parameters. We also investigate the role of secondary spin and find no significant correlation between the secondary spin and the scalar charge. Notably, up to a mass ratio of $10^{-4}$, the secondary spin itself remains unconstrained even in the pure-GR case, in contrast with previous claims in the literature. Finally, we show that modelling scalar emission with a leading-order dipolar post-Newtonian approximation -- for quasi-circular inspirals into a non-spinning primary -- introduces negligible systematic errors relative to fully relativistic scalar fluxes.

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arXiv2026

An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis

The Wilson-Daubechies-Meyer (WDM) time-frequency transform has been widely used in gravitational-wave astronomy, yet a self-contained, mathematically explicit reference for practitioners remains lacking. This is especially true for those wishing to adopt the transform in modern Python and JAX inference workflows. We present wdm_transform, an open-source Python package implementing the WDM wavelet-packet time-frequency transform, and document its mathematical foundations, statistical properties, and practical implementation for gravitational-wave data analysis. The package supplies NumPy and JAX backends, both transforms (forward and inverse) validated to floating-point precision, with the JAX backend enabling GPU-accelerated transforms of million-point data streams in tens of milliseconds. As a worked example, we verify that the WDM-domain likelihood reproduces frequency-domain posteriors for a resolved LISA galactic binary under a shared stationary noise model, confirming numerical equivalence of the two representations in that controlled setting. This work paves the way for systematic optimisation of WDM tilings, a particularly promising direction for the non-stationary noise, stochastic backgrounds, and data gaps anticipated in future detectors, and for direct comparisons with alternative time-frequency representations needed to meet the challenges of future gravitational-wave data analysis.

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arXiv2026

Systematic errors in fast relativistic waveforms for Extreme Mass Ratio Inspirals

Accurate modeling of \gls{EMRIs} is essential for extracting reliable information from future space-based gravitational wave observatories. Fast waveform generation frameworks adopt an offline/online architecture, where expensive relativistic computations (e.g. self-force and black hole perturbation theory) are performed offline, and waveforms are generated rapidly online via interpolation across a multidimensional parameter space. In this work, we investigate potential sources of error that result in systematic bias in these relativistic waveform models, focusing on radiation-reaction fluxes. Two key sources of systematics are identified: (i) the intrinsic inaccuracy of the flux data, for which we focus on the truncation of the multipolar mode sum, and (ii) interpolation errors from transitioning to the online stage. We quantify the impact of mode-sum truncation and analyze interpolation errors by using various grid structures and interpolation schemes. For circular orbits in Kerr spacetime with spins larger than $a \geq 0.9$, we find that $\ell_{\text{max}} \geq 30$ is required for the necessary accuracy. We also develop an efficient Chebyshev interpolation scheme, achieving the desired accuracy level with significantly fewer grid points compared to spline-based methods. For circular orbits in Kerr spacetimes, we demonstrate via Bayesian studies that interpolating the flux to a maximum global relative error that is equal to the small mass ratio is sufficient for parameter estimation purposes. For 4-year long quasi-circular EMRI signals with SNRs $= \mathcal{O}(100)$ and mass-ratios $10^{-4}-10^{-6}$, a global relative error of $10^{-6}$ yields mismatches $<10^{-3}$ and negligible parameter estimation biases.

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arXiv2026

LISA science ground segment conventions

This document sets out the conventions used for data simulations, waveforms, and analysis pipelines within the Distributed Data Processing Centre (DDPC) of the Laser Interferometer Space Antenna (LISA). It can also be considered a best practice guide for all publications related to the LISA mission. Topics covered include time-to-frequency transformations, gravitational-wave source parametrization, the instrumental response to gravitational waves, time-delay interferometry, and reference frame definitions.

astro-ph.IM↗

arXiv2026

First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals

This work investigates the impact of streams of transient, non-Gaussian noise artifacts or "glitches" on the parameter estimation of extreme mass ratio inspirals (EMRI) in the Laser Interferometer Space Antenna (LISA). Glitches cause biased and less precise inference for short-duration signals such as massive black hole binaries, but their effect on long-lived sources such as EMRIs has not been quantified. Using simulated LISA observations containing injected EMRIs and streams of shapelet-based glitches drawn from the LISA Pathfinder catalog, we estimate the glitch-induced parameter biases and uncertainties through a Fisher-matrix-based analysis whose accuracy we verify with Markov-Chain Monte Carlo. We find that moderately mitigated glitch streams i.e. ones containing only glitches of up to moderate SNRs ($ρ\lesssim 90$) induce negligible to minor biases $[\sim0.04σ,\sim0.6σ]$ in the inferred EMRI parameters. In contrast, weakly mitigated glitch streams containing higher-SNR events ($ρ\lesssim 400$) can produce biases nearing $1σ$. These results demonstrate that, when compared to inference of other sources such as massive black hole binaries, EMRI inference is notably more robust to glitches. We stress that at least some amount of glitch modeling and mitigation remains essential for unbiased EMRI analyses in the LISA era.

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arXiv2026

Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge

Extreme Mass Ratio Inspirals (EMRIs) are key sources for the future space-based gravitational wave detector LISA, and are considered promising probes of fundamental physics. Here, we present the first complete Bayesian analysis of EMRI signals in theories with an additional massless scalar, which could arise in an extension of General Relativity or of the Standard Model of Particle Physics. We develop a waveform model accurate at adiabatic order for equatorial eccentric orbits around spinning black holes. Using full Bayesian inference, we forecast LISA's ability to probe the presence of new fundamental fields with EMRI observations.

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arXiv2025

The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes

Observations of gravitational-wave signals emitted by compact binary inspirals provide unique insights into their properties, but their analysis requires accurate and efficient waveform models. Intermediate- and extreme-mass-ratio inspirals (I/EMRIs), with mass ratios $q \gtrsim 10^2$, are promising sources for future detectors such as the Laser Interferometer Space Antenna (LISA). Modelling waveforms for these asymmetric-mass binaries is challenging, entailing the tracking of many harmonic modes over thousands to millions of cycles. The FastEMRIWaveforms (FEW) modelling framework addresses this need, leveraging precomputation of mode data and interpolation to rapidly compute adiabatic waveforms for eccentric inspirals into zero-spin black holes. In this work, we extend FEW to model eccentric equatorial inspirals into black holes with spin magnitudes $|a| \leq 0.999$. Our model supports eccentricities $e < 0.9$ and semi-latus recta $p < 200$, enabling the generation of long-duration IMRI waveforms, and produces waveforms in $\sim 100$ ms with hardware acceleration. Characterising systematic errors, we estimate that our model attains mismatches of $\sim 10^{-5}$ (for LISA sensitivity) with respect to error-free adiabatic waveforms over most of parameter space. We find that kludge models introduce errors in signal-to-noise ratios (SNRs) as great as $^{+60\%}_{-40\%}$ and induce marginal biases of up to $\sim 1σ$ in parameter estimation. We show LISA's horizon redshift for I/EMRI signals varies significantly with $a$, reaching a redshift of $3$ ($15$) for EMRIs (IMRIs) with only minor $(\sim10\%)$ dependence on $e$ for an SNR threshold of 20. For signals with SNR $\sim 50$, spin and eccentricity-at-plunge are measured with uncertainties of $δa \sim 10^{-7}$ and $δe_f \sim 10^{-5}$. This work advances the state-of-the-art in waveform generation for asymmetric-mass binaries.

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arXiv2025

The impact of missing data on the construction of LISA Time Delay Interferometry Michelson variables

We investigate the impact of missing input data on the construction of second-generation Time Delay Interferometry (TDI) variables, which enable data analysis for the Laser Interferometer Space Antenna (LISA). TDI relies on the introduction of precise time delays into the raw interferometric data streams before they are combined to suppress otherwise dominant laser phase noise. We show that a single missing sample, corresponding to 0.25 s of data, will result in an effective data gap of approximately 90 s in the second-generation TDI output if further measures are not taken. This additional gap is largely independent of the initial gap duration, but increases linearly with the order of the fractional-delay filter used for the computations. For a realistic gap scenario, incorporating both planned and unplanned data interruptions consistent with a target duty cycle of ~84%, we find that frequent, short-duration gaps (e.g., a total of 1000 per year, each of which have short durations ~ 100 s) could result in an additional loss in the TDI variables of about one day per year corresponding to a ~0.3% reduction in duty cycle. This amounts to a loss of approximately one day of LISA data suitable for the global-fit per year.

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arXiv2025

A note on the conversion of orbital angles for extreme mass ratio inspirals

We outline a practical scheme for converting between three commonly used sets of phases to describe the trajectories of extreme mass ratio inspirals; quasi-Keplerian angles, Mino time action-angles, and Boyer-Lindquist time action-angles (as utilised by the FastEMRIWaveform package). Conversion between Boyer-Lindquist time action angles and quasi-Keplerian angles is essential for the construction of a source frame for adiabatic inspirals that can be related to the source frames used by other gravitational wave source modelling techniques. While converting from quasi-Keplerian angles to Boyer-Lindquist time action angles via Mino time action-angles can be done analytically, the same does not hold for the converse, and so we make use of an efficient numerical root-finding method. We demonstrate the efficacy of our scheme by comparing two calculations for an eccentric and inclined geodesic orbit in Kerr spacetime using two different sets of orbital angles. We have made our implementations available in Mathematica, C, and Python.

gr-qc↗

arXiv2025

Test for LISA foreground Gaussianity and stationarity: extreme mass-ratio inspirals

Extreme Mass Ratio Inspirals (EMRIs) are key observational targets for the Laser Interferometer Space Antenna (LISA) mission. Unresolvable EMRI signals contribute to the formation of a gravitational wave background (GWB). Characterizing the statistical features of the GWB from EMRIs is of great importance, as EMRIs will ubiquitously affect large segments of the inference scheme. In this work, we apply a frequentist test for GWB Gaussianity and stationarity, exploring three astrophysically-motivated EMRI populations. We construct the resulting signal by combining state-of-the-art EMRI waveforms and a detailed description of the LISA response with time-delay interferometric variables.Depending on the brightness of the GWB, our analysis demonstrates that the resultant EMRI foregrounds show varying degrees of departure from the usual statistical assumptions that the GWBs are both Gaussian and Stationary. If the GWB is non-stationary with non-Gaussian features, this will challenge the robustness of Gaussian-likelihood model, when applied to global inference results, e.g. foreground estimation, background detection, and individual-source parameters reconstruction.

astro-ph.HE↗

arXiv2025

Mind the gap: addressing data gaps and assessing noise mismodeling in LISA

Due to the sheer complexity of the Laser Interferometer Space Antenna (LISA) space mission, data gaps arising from instrumental irregularities and/or scheduled maintenance are unavoidable. Focusing on merger-dominated massive black hole binary signals, we test the appropriateness of the Whittle-likelihood on gapped data in a variety of cases. From first principles, we derive the likelihood valid for gapped data in both the time and frequency domains. Cheap-to-evaluate proxies to p-p plots are derived based on a Fisher-based formalism, and verified through Bayesian techniques. Our tools allow to predict the altered variance in the parameter estimates that arises from noise mismodeling, as well as the information loss represented by the broadening of the posteriors. The result of noise mismodeling with gaps is sensitive to the characteristics of the noise model, with strong low-frequency (red) noise and strong high-frequency (blue) noise giving statistically significant fluctuations in recovered parameters. We demonstrate that the introduction of a tapering window reduces statistical inconsistency errors, at the cost of less precise parameter estimates. We also show that the assumption of independence between inter-gap segments appears to be a fair approximation even if the data set is inherently coherent. However, if one instead assumes fictitious correlations in the data stream, when the data segments are actually independent, then the resultant parameter recoveries could be inconsistent with the true parameters. The theoretical and numerical practices that are presented in this work could readily be incorporated into global-fit pipelines operating on gapped data.

gr-qc↗

arXiv2024

Accuracy Requirements: Assessing the Importance of First Post-Adiabatic Terms for Small-Mass-Ratio Binaries

We investigate the impact of post-adiabatic (1PA) terms on parameter estimation for extreme and intermediate mass-ratio inspirals using state-of-the-art waveform models. Our analysis is the first to employ Bayesian inference to assess systematic errors for 1PA waveforms. We find that neglecting 1PA terms introduces significant biases for the (small) mass ratio $ε\gtrsim 10^{-5}$ for quasi circular orbits in Schwarzschild spacetime, which can be mitigated with resummed 3PN expressions at 1PA order. Moreover, we show that the secondary spin is strongly correlated with the other intrinsic parameters, and it can not be constrained for $ε\lesssim 10^{-5}$. Finally, we highlight the need for addressing eccentric waveform systematics in the small-mass-ratio regime, as they yield stronger biases than the circular limit in both intrinsic and extrinsic parameters.

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arXiv2024

Calibrating approximate Bayesian credible intervals of gravitational-wave parameters

Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW) data analysis, fast-to-evaluate but approximate waveform models of astrophysical GW signals are sometimes used in lieu of more accurate models to infer properties of a true GW signal buried within detector noise. In addition, a Fisher-information-based normal approximation to the posterior distribution can also be used to conduct inference in bulk, without the need for extensive numerical calculations such as Markov chain Monte Carlo (MCMC) simulations. Such approximations can generally lead to an inaccurate posterior distribution with poor statistical coverage of the true posterior. In this article, we present a novel calibration procedure that calibrates the credible sets for a family of approximate posterior distributions, to ensure coverage of the true posterior at a level specified by the analyst. Tools such as autoencoders and artificial neural networks are used within our calibration model to compress the data (for efficiency) and to perform tasks such as logistic regression. As a proof of principle, we demonstrate our formalism on the GW signal from a high-mass binary black hole merger, a promising source for the near-future space-based GW observatory LISA.

gr-qc↗

arXiv2023

Strongly-Lensed Extreme Mass-ratio Inspirals

In this work, we investigate detection rates and parameter estimation of strongly-lensed extreme mass-ratio inspirals (LEMRIs) in the context of the Laser Interferometer Space Antenna (LISA). Our results indicate that LEMRIs constitute a new gravitational-wave target signal for LISA, with detection rates ranging from zero to $\sim 40$ events over a four year-observation, and that it is possible to reveal and characterize LEMRIs at redshift $z \gtrsim 1$. We finally show that one LEMRI observation with identified host galaxy may yield percent constraints or better on the Hubble constant.

astro-ph.CO↗

arXiv2021

Noisy neighbours: inference biases from overlapping gravitational-wave signals

Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working within the linear signal approximation, we describe generic metrics to predict inference biases on GW source parameters in the presence of confusion noise from unfitted foregrounds, from overlapping signals that coalesce close in time to one another, and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simplified, yet realistic, scenarios appropriate to third-generation ground-based (Einstein Telescope) and space-based (LISA) detectors, and demonstrate its validity against Monte-Carlo simulations. We find it to be a reliable tool to cheaply predict the extent and direction of the biases. Finally, we show how this formalism can be used to correct for biases that arise in the sequential characterisation of multiple sources in a single data set, which could be a valuable tool to use within a global-fit analysis pipeline.

gr-qc↗

arXiv2020

Constraining the spin parameter of near-extremal black holes using LISA

We describe a model that generates first order adiabatic EMRI waveforms for quasi-circular equatorial inspirals of compact objects into rapidly rotating (near-extremal) black holes. Using our model, we show that LISA could measure the spin parameter of near-extremal black holes (for $a \gtrsim 0.9999$) with extraordinary precision, $\sim$ 3-4 orders of magnitude better than for moderate spins, $a \sim 0.9$. Such spin measurements would be one of the tightest measurements of an astrophysical parameter within a gravitational wave context. Our results are primarily based off a Fisher matrix analysis, but are verified using both frequentest and Bayesian techniques. We present analytical arguments that explain these high spin precision measurements. The high precision arises from the spin dependence of the radial inspiral evolution, which is dominated by geodesic properties of the secondary orbit, rather than radiation reaction. High precision measurements are only possible if we observe the exponential damping of the signal that is characteristic of the near-horizon regime of near-extremal inspirals. Our results demonstrate that, if such black holes exist, LISA would be able to successfully identify rapidly rotating black holes up to $a = 1-10^{-9}$ , far past the Thorne limit of $a = 0.998$.

gr-qc↗

arXiv2020

Transition from Inspiral to Plunge: A Complete Near-Extremal Trajectory and Associated Waveform

We extend the Ori and Thorne (OT) procedure to compute the transition from an adiabatic inspiral into a geodesic plunge for any spin, with emphasis on near-extremal ones. Our analysis revisits the validity of the approximations made in OT. In particular, we discuss possible effects coming from eccentricity and non-geodesic past-history of the orbital evolution. We find three different scaling regimes according to whether the mass ratio is much smaller, of the same order or much larger than the near extremal parameter describing how fast the primary black hole rotates. Eccentricity and non-geodesic past-history corrections are always sub-leading, indicating that the quasi-circular approximation applies throughout the transition regime. However, we show that the OT assumption that the energy and angular momentum evolve linearly with proper time must be modified in the near-extremal regime. Using our transition equations, we describe an algorithm to compute the full worldline in proper time for an extreme mass ratio inspiral (EMRI) and the resultant gravitational waveform in the high spin limit.

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