Detectability of the subdominant mode in a binary black hole ringdown
Bibliographic record
Abstract
The ringdown is the late part of the postmerger signature emitted during the coalescence of two black holes and comprises a superposition of quasinormal modes. Within the general theory of relativity, the no-hair theorem for black holes states that the frequencies and the damping times of these modes are entirely determined by the mass and the angular momentum of the final Kerr black hole. Detection of multiple ringdown modes in the gravitational wave signal emitted during a binary black hole coalescence would allow us to validate the no-hair theorem with observations. The signal-to-noise ratio of the black hole ringdown and the amplitude of the subdominant modes to the dominant mode determine the detectability of the subdominant mode. We use Bayesian inference to investigate the interplay between these two factors towards their detectability. We systematically vary the two factors in a set of simulated analytical ringdown signals to infer the minimum signal-to-noise ratio needed in a ringdown signal for performing black hole spectroscopy. Our estimates on the minimum signal strength required to perform black hole spectroscopy as a function of amplitude ratio allows us to gain insight into the kind of signals that will be promising for black hole spectroscopy.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".