arXiv subjects

Christian E. A. Chapman-Bird

Publications and source records attributed to Christian E. A. Chapman-Bird.

8 recordsLinked to original sources

arXiv2026

Scalable Dark Siren Cosmology with gwcosmo: GPU Acceleration, Validation and Systematics

As the number of confident gravitational-wave detections grows, population-level hierarchical analyses face increasing computational costs. Dark-siren cosmological inference integrates over the localisation volume of each gravitational-wave source. To remain feasible without discarding information from the quieter but more numerous sources in the catalogue, significant efficiency improvements are vital for analysis pipelines. In this work, we present an upgraded version of the cosmological inference pipeline gwcosmo, which leverages vectorisation on graphics processing units to process the entire gravitational-wave catalogue in parallel with each iteration. This new implementation achieves a speed-up of 1000 times over the previous version, facilitating analyses of O5-like numbers of GW events on wall-clock timescales of hours. Our results demonstrate the scalability of the gwcosmo pipeline, specifically its ability to handle the increasing computational load of expanding event catalogues, positioning it as a vital tool for future advances in dark-siren cosmology.

astro-ph.CO↗

arXiv2026

Global time-frequency search for stellar-mass binary black holes in LISA

We present a complete pipeline for detecting and characterizing gravitational waves (GWs) produced by the inspiral of stellar-mass binary black holes in data from the Laser Interferometer Space Antenna (LISA). The analysis framework relies on an efficient time-frequency implementation of an adaptive semi-coherent detection statistic, which we show to be robust against non-stationary noise and the presence of gaps of varying duration and cadence. The search is able to detect signals with coherent signal-to-noise ratios as low as $\approx 11-14$ over the full parameter space of binary black holes with spins aligned to the orbital angular momentum and orbital eccentricity $\le 0.01$ when deployed on the 2-year-long LISA Data Challenge Yorsh. The search can be run within a day using $\approx 40$ GPUs. The techniques presented here have wider applications in GW astronomy, in particular the search for extreme-mass-ratio inspirals in LISA data.

gr-qc↗

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

Revealing massive black hole astrophysics: The potential of hierarchical inference with extreme mass-ratio inspiral observations

Gravitational waves from extreme mass-ratio inspirals (EMRIs) will enable sub-percent measurements of massive black hole parameters and provide access to the demographics of compact objects in galactic nuclei. During the LISA mission, multiple EMRIs are expected to be detected, allowing statistical studies of massive black hole populations and their formation pathways. We perform hierarchical Bayesian inference on simulated EMRI catalogues to assess how well LISA could constrain the astrophysical population using parametrised population models. We test our inference framework on a variety of populations, including heterogeneous and homogeneous mixtures of parametrised subpopulations, and scenarios in which the assumed model is deliberately misspecified. Our results show that population parameters governing distributions with sharp features can be tightly constrained. Mixed populations can be disentangled with as few as $\sim20$ detections, and even with model misspecification, the inference retains sensitivity to key population features. These results demonstrate the capabilities and limitations of EMRI population inference, providing guidance for constructing realistic astrophysical population models for LISA analysis.

astro-ph.HE↗

arXiv2026

Constraints on the extreme mass-ratio inspiral population from LISA data

Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Interferometer Space Antenna (LISA). A sufficiently large number of EMRI observations will provide unique insights into the massive black hole population. We have developed a hierarchical Bayesian inference framework capable of constraining the parameters of the EMRI population, accounting for selection biases. We leverage the capacity of a feed-forward neural network as an emulator, enabling detectability calculations of $\sim10^5$ EMRIs in a fraction of a second, speeding up the likelihood evaluation by $\gtrsim6$ orders of magnitude. We validate our framework on a phenomenological EMRI population model. This framework enables studies of how well we can constrain EMRI population parameters, such as the slope of both the massive and stellar-mass black hole mass spectra and the branching fractions of different formation channels, allowing further investigation into the evolution of massive black holes.

gr-qc↗

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.

gr-qc↗

arXiv2024

Rapid determination of LISA sensitivity to extreme mass ratio inspirals with machine learning

Gravitational wave observations of the inspiral of stellar-mass compact objects into massive black holes (MBHs), extreme mass ratio inspirals (EMRIs), enable precision measurements of parameters such as the MBH mass and spin. The Laser Interferometer Space Antenna is expected to detect sufficient EMRIs to probe the underlying source population, testing theories of the formation and evolution of MBHs and their environments. Population studies are subject to selection effects that vary across the EMRI parameter space, which bias inference results if unaccounted for. This bias can be corrected, but evaluating the detectability of many EMRI signals is computationally expensive. We mitigate this cost by (i) constructing a rapid and accurate neural network interpolator capable of predicting the signal-to-noise ratio of an EMRI from its parameters, and (ii) further accelerating detectability estimation with a neural network that learns the selection function, leveraging our first neural network for data generation. The resulting framework rapidly estimates the selection function, enabling a full treatment of EMRI detectability in population inference analyses. We apply our method to an astrophysically motivated EMRI population model, demonstrating the potential selection biases and subsequently correcting for them. Accounting for selection effects, we predict that LISA will measure the MBH mass function slope to a precision of 8.8%, the CO mass function slope to a precision of 4.6%, the width of the MBH spin magnitude distribution to a precision of 10% and the event rate to a precision of 12% with EMRIs at redshifts below z=6.

astro-ph.HE↗

arXiv2024

Fast and Fourier: Extreme Mass Ratio Inspiral Waveforms in the Frequency Domain

Extreme Mass Ratio Inspirals (EMRIs) are one of the key sources for future space-based gravitational wave interferometers. Measurements of EMRI gravitational waves are expected to determine the characteristics of their sources with sub-percent precision. However, their waveform generation is challenging due to the long duration of the signal and the high harmonic content. Here, we present the first ready-to-use Schwarzschild eccentric EMRI waveform implementation in the frequency domain for use with either graphics processing units (GPUs) or central processing units (CPUs). We present the overall waveform implementation and test the accuracy and performance of the frequency domain waveforms against the time domain implementation. On GPUs, the frequency domain waveform takes in median $0.044$ seconds to generate and is twice as fast to compute as its time domain counterpart when considering massive black hole masses $\geq 2 \times 10^6 \,{\rm M_\odot}$ and initial eccentricities $e_0 > 0.2$. On CPUs, the median waveform evaluation time is $5$ seconds, and it is five times faster in the frequency domain than in the time domain. Using a sparser frequency array can further speed up the waveform generation, reaching up to $ 0.3$ seconds. This enables us to perform, for the first time, EMRI parameter inference with fully relativistic waveforms on CPUs. Future EMRI models which encompass wider source characteristics (particularly black hole spin and generic orbit geometries) will require significantly more harmonics. Frequency-domain models will be essential analysis tools for these astrophysically realistic and important signals.

gr-qc↗