Multimethod signal processing for comprehensive tune coupling characterization at Canadian Light Source
Bibliographic record
Abstract
This study compares Fast Fourier Transform (FFT), Power Spectral Density (PSD), and Wavelet Analysis for detecting tune coupling at the Canadian Light Source (CLS). Data were analyzed for low coupling, 1.4%, and 2.5% high coupling regimes, focusing on frequency identification and amplitude stability in X and Y directions. FFT revealed ~15% amplitude fluctuations, complicating tune identification. PSD provided better stability, with only 4% amplitude variations. Both methods were computationally efficient, with FFT taking, 0.0103 seconds and PSD, 0.0108 seconds per calculation. Wavelet analysis preserved temporal-frequency relationships, revealing delays between X and Y frequencies of 2.38 to 4.77 microseconds in the 1.4% regime and peak periods around 18 microseconds. In high coupling, X frequencies preceded Y frequencies, with dominant frequencies showing higher amplitudes than perturbed ones. These findings demonstrate PSD's stability for tune measurements and Wavelet Analysis's ability to capture temporal dynamics, providing insights to enhance beam stability in accelerator systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".