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Record W4416204750 · doi:10.1103/scbp-75pf

Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly spinning black holes

2025· article· en· W4416204750 on OpenAlexafffund
C. Chapman-Bird, Lorenzo Speri, Zachary Nasipak, Ollie Burke, Michael L. Katz, Alessandro Santini, Shubham Kejriwal, Philip Lynch, Josh Mathews, Hassan Khalvati, Jonathan E. Thompson, Soichiro Isoyama, Scott A. Hughes, Niels Warburton, Alvin J. K. Chua, Maxime Pigou

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of GuelphPerimeter Institute
FundersInstitut Périmètre de physique théoriqueNational Science FoundationRoyal SocietyEuropean Space AgencyEuropean Research CouncilMinistry of Education - SingaporeUK Research and InnovationUK Space AgencyNational University of SingaporeMinistry of Colleges and UniversitiesGovernment of CanadaTier 1 Wall StreetDairy Research IrelandLisa and Douglas Goldman Fund
KeywordsWaveformSuperposition principleAdiabatic processBinary numberInterpolation (computer graphics)HarmonicBlack hole (networking)Orbit (dynamics)Parameter space

Abstract

fetched live from OpenAlex

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 ≳ 10 2 , are promising sources for future detectors such as the Laser Interferometer Space Antenna (LISA). Modeling waveforms for these asymmetric-mass binaries is challenging, entailing the tracking of many harmonic modes over thousands to millions of cycles. The FastEMRIWaveforms () modeling 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 to model eccentric equatorial inspirals into black holes with spin magnitudes | a | ≤ 0.999 . Our model supports eccentricities e ≤ 0.9 and semilatus recta p ≤ 200 , enabling the generation of long-duration IMRI waveforms, and produces waveforms in ∼ 100 ms with hardware acceleration. Characterizing systematic errors, we estimate that our model attains mismatches of ∼ 10 − 5 (for LISA sensitivity) with respect to error-free adiabatic waveforms over the majority of the parameter space. We find that kludge models can introduce errors in signal-to-noise ratios (SNRs) as great as − 40 % + 60 % and induce marginal biases of up to ∼ 1 σ in parameter estimation. We show that LISA’s horizon redshift for I/EMRI signals varies significantly with a , reaching a redshift of 3 (15) for EMRIs (IMRIs) with only minor ( ∼ 10 % ) dependence on e for an SNR threshold of 20. For signals with SNR ∼ 50 , spin and eccentricity at plunge are measured with uncertainties of δ a ∼ 10 − 7 and δ e f ∼ 10 − 5 . This work advances the state of the art in waveform generation for asymmetric-mass binaries, providing open-source tools for the investigation of I/EMRI astrophysics and data analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.460
Teacher spread0.443 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2025
Admission routes2
Has abstractyes

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