Detecting unmodeled, source-dependent signals in gravitational waves with SCoRe
Bibliographic record
Abstract
New physics and systematic errors can lead to deviations between the models used to analyze gravitational wave data and the actual signal. Such deviations will generally be correlated between detectors and manifest differently across the gravitational wave source parameter space. The previously introduced SCoRe framework uses these features to distinguish these deviations from noise and extract physical information from their source-dependent variation. In this work, we further analyze the hierarchical component of the method---we include the expected dependence of the deviations on the source parameters into the inference process, obtaining more physically informative results. As a specific example, we study a deviation that scales as a power law of the mass scale of black hole binaries---as, for example, in the effective field theory of gravity. We show how the signal-to-noise ratio of the cross-correlated residual power can be used to recover the power-law index. We demonstrate how both the distribution in source and deviation strength determine which region of source parameter space influences the inference most. Finally, we forecast the constraint on the power law index for a network of two Cosmic Explorer-like detectors with a year of observation period.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".