GW231123 ringdown: interpretation as multimodal Kerr signal
Bibliographic record
Abstract
GW231123 is a short-duration, low-frequency gravitational wave signal consistent with a binary black hole coalescence and dominated by the merger-ringdown regime due to the high mass of the source. We demonstrate that fits of this ringdown signal using two quasinormal modes are statistically preferred over single-mode fits, for a broad range of fit start times. We also find that two-mode fits give remnant mass and spin measurements consistent with those of the inspiral-merger-ringdown model NRSur7dq4, whereas one-mode fits struggle to do so. Agreement of our fits with those of NRSur7dq4 is achieved by labeling the two quasinormal modes as the ${(\ell,m)=(2,2)}$ and ${(2,0)}$ Kerr prograde fundamental modes. However, we find some indications that fits with the ${(2,1)}$ quasinormal mode instead of the ${(2,0)}$ mode may describe the data better, hinting at possible NRSur7dq4 error or other systematics. When fitting at early times near the estimated peak strain, we find that the inclusion of a third mode, an ${(\ell,m,n)=(2,2,1)}$ prograde overtone, improves consistency with fits at later times. Finally, we perform a test of general relativity by searching for deviations from the Kerr frequency spectrum. Setting issues of systematics aside, we validate the Kerr frequency and damping rate spectrum to within $\pm10\%$ at the 90$\%$ credible level using a fundamental mode fit, and we also report $\pm8\%$ constraints using a model with fundamental modes and an overtone fit at times near the peak strain. Understanding the systematic errors that may be affecting the most accurate analyses of GW231123 is crucial in the context of population and binary formation studies -- our ${(2,1)}$ mode fits return a significantly higher remnant mass and spin than all available inspiral-merger-ringdown models including NRSur7dq4, and this difference in parameter estimates may have astrophysical implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".