Dynamic Tuner Development for Medium β Superconducting Elliptical Cavities
Bibliographic record
Abstract
The Facility for Rare Isotope Beams (FRIB) is developing a 5-cell 644 MHz β_{o}pt=0.65 elliptical cavity for a future linac energy upgrade to 400 MeV/u for the heaviest uranium ions. Superconducting elliptical cavities operated in continuous wave, such as the ones for FRIB, are prone to microphonics which can excite mechanical modes of the cavities. It has been shown that the detuning due to microphonics can be mitigated with the use of piezo actuators (fast tuner) as opposed to the costly option of increasing the input RF power. The FRIB slow/fast dynamic tuner will be based on the Fermilab experience with similar tuners like those developed for the linac coherent light source (LCLS) II and proton improvement plan (PIP) II. This paper will present the results of tuner properties on the bench.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".