Black Hole Spectroscopy with Coherent Mode Stacking
Bibliographic record
Abstract
The measurement of multiple ringdown modes in gravitational waves from binary black hole mergers will allow for testing the fundamental properties of black holes in general relativity and to constrain modified theories of gravity. To enhance the ability of Advanced LIGO/Virgo to perform such tasks, we propose a coherent mode stacking method to search for a chosen target mode within a collection of multiple merger events. We first rescale each signal so that the target mode in each of them has the same frequency and then sum the waveforms constructively. A crucial element to realize this coherent superposition is to make use of a priori information extracted from the inspiral-merger phase of each event. To illustrate the method, we perform a study with simulated events targeting the ℓ=m=3 ringdown mode of the remnant black holes. We show that this method can significantly boost the signal-to-noise ratio of the collective target mode compared to that of the single loudest event. Using current estimates of merger rates, we show that it is likely that advanced-era detectors can measure this collective ringdown mode with one year of coincident data gathered at design sensitivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".