Performance Study of the Vibrating Wire Technique to Determine Longitudinal Magnetic Field Profile Using Scans to High Wire Harmonic
Bibliographic record
Abstract
Particle accelerator laboratories, which enable world-class research across many scientific fields, depend on the magnets used to manipulate their particle beams for successful operation. The community employs various techniques, typically based on Hall probes and induction sensors/coils, to verify the performance of accelerator magnets. Conventional Hall probe systems cannot be deployed, or require significant modification, when the transverse access around a magnet is restricted; meanwhile, moving wire/coil systems tend to provide information only on the B field’s integral. This research builds upon a vibrating wire setup first commissioned to locate the magnetic center of quadrupole magnets. Scans up to the n=200 wire harmonic (∼10 kHz drive frequency) were measured to reconstruct the B field across a vibrating wire strung through a test magnet. New software was developed to systematically process the many wire harmonics needed for a detailed field reconstruction. This research investigated the speed and precision of the measurement, identifying limitations due to both instrumentation and nonlinear wire behaviour at very high harmonic. The vibrating wire data agreed with a reference Hall probe scan on the order of 6%; roughly 0.7% RMS error persisted after calibrating the vibrating wire data to the reference scan via scaling factor.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".