Including mode mixing in a higher-multipole model for gravitational waveforms from nonspinning black-hole binaries
Bibliographic record
Abstract
As gravitational-wave (GW) observations of binary black holes are becoming a precision tool for physics and astronomy, several subdominant effects in the GW signals need to be accurately modeled. Previous studies have shown that neglecting subdominant modes in the GW templates causes an unacceptable loss in detection efficiency and large systematic errors in the estimated parameters for binaries with large mass ratios. Our recent work [Mehta et al., Phys. Rev. D 96, 124010 (2017)] constructed a phenomenological gravitational waveform family for nonspinning black-hole binaries that includes subdominant spherical harmonic modes $(\ensuremath{\ell}=2,m=\ifmmode\pm\else\textpm\fi{}1)$, $(\ensuremath{\ell}=3,m=\ifmmode\pm\else\textpm\fi{}3)$, and $(\ensuremath{\ell}=4,m=\ifmmode\pm\else\textpm\fi{}4)$ in addition to the dominant quadrupole mode, $(\ensuremath{\ell}=2,m=\ifmmode\pm\else\textpm\fi{}2)$. In this article, we construct analytical models for the ($\ensuremath{\ell}=3,m=\ifmmode\pm\else\textpm\fi{}2$) and ($\ensuremath{\ell}=4,m=\ifmmode\pm\else\textpm\fi{}3$) modes and include them in the existing waveform family. Accurate modeling of these modes is complicated by the mixing of multiple spheroidal harmonic modes. We develop a method for accurately modeling the effect of mode mixing, thus producing an analytical waveform family that has faithfulness greater than 99.6%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".