Comment on: "Low significance of evidence for black hole echoes in\n gravitational wave data"
Bibliographic record
Abstract
In a recent publication (1612.00266), we demonstrated that the events in the\nfirst observing run of the Advanced LIGO gravitational wave observatory (aLIGO\nO1) showed tentative evidence for repeating "echoes from the abyss" caused by\nPlanck-scale structure near black hole horizons. By considering a\nphenomenological echo model, we showed that the pure noise hypothesis is\ndisfavored with a p-value of 1%, i.e. higher amplitude for echoes than those in\naLIGO O1 events are only recovered in 1% of random noise realizations. A recent\npreprint by Westerweck, et al. (1712.09966) provides a careful re-evaluation of\nour analysis which claims "a reduced statistical significance ... entirely\nconsistent with noise". It is a mystery to us why the authors make such a\nstatement, while they also find a p-value of 2 $\\pm$ 1% (given the Poisson\nerror in their estimate) for the same model and dataset. This is p-erfectly\nconsistent with our results, which would be commonly considered as disfavoring\nthe null hypothesis, or "moderate to significant" evidence for "echoes".\nWesterweck, et al. also point to diversity of the observed echo properties as\nevidence for statistical fluke, but such a diversity is neither unique nor\nsurprising for complex physical phenomena in nature.\n
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.017 |
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".