Efficient fully precessing gravitational waveforms for binaries with neutron stars
Bibliographic record
Abstract
We construct an efficient frequency domain waveform for generic circular compact object binaries that include neutron stars. The orbital precession is solved on the radiation reaction timescale (and then transformed to the frequency domain), which is used to map the nonprecessional waveform from the source frame of the binary to the lab frame. The treatment of orbital precession is different from that for precessional binary black holes, as ${\ensuremath{\chi}}_{\mathrm{eff}}$ is no longer conserved due to the spin-induced quadrupole moments of neutron stars. We show that the new waveform achieves $\ensuremath{\le}{10}^{\ensuremath{-}4}$ difference in terms of the one minus the overlap compared with waveforms generated by numerically evolved precession for neutron star-black hole systems for $\ensuremath{\ge}90%$ configurations with component mass/spin magnitude assumed in the analysis and randomized initial spin directions. We expect this waveform to be useful to test the nature of the mass-gap objects similar to the one discovered in GW 190814 by measuring their spin-induced quadrupole moments, as it is possible that these mass-gap objects are rapidly spinning. It is also applicable for the tests of black hole mimickers in precessional binary black hole events, if the black hole mimicker candidates have nontrivial spin-induced quadrupole moments.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".