Improvement of accelerating gradients in niobium quarter wave resonators
Bibliographic record
Abstract
Abstract Systematic studies have been performed on the effect of the surface processing techniques for improving accelerating gradients in superconducting niobium quarter wave resonators (QWR). These include high pressure rinsing (HPR), high temperature heat treatment of cavities and helium pulse processing. Tests done after HPR have not only shown a reduction in field emission in the cavities at high accelerating gradients but also an improvement in the low field quality factor ( Q ). The effect of the high temperature (650 °C) heat treatment of jacketed QWRs (QWR with the outer helium vessel) on the cavity gradients has also been investigated. This was performed for two different QWR designs and a substantial improvement in performance has been observed in both the cases. The increase in gradients is beyond that due to hydrogen degassing alone. Helium pulse processing during 4 K tests has been tried out on several cavities and its effect on the quality factor at both high and low gradients has been observed. This technique has been found to be useful for those resonators which have a high Q at lower fields but are limited due to the field emission at higher gradients. They have exhibited a marked improvement in the high field Q -slope over and above that obtained with conventional pulse processing under high vacuum. A comprehensive overview of all these developments carried out over the past few years has been reported.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".