Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly spinning black holes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".