Summary of the Snowmass’21 Workshop on High Power Cyclotrons and FFAs
Bibliographic record
Abstract
In this talk, we summarize the presentations and findings of the "Workshop on High Power Cyclotrons and FFAs" that we held online in September 2021. The workshop was held as part of the 2021 Snowmass Community Exercise, in which the US particle physics community came together in a year-long effort to provide suggestions for a long-term strategy for the field, and the "Accelerators for Neutrinos" subpanel thereof. Topics that were discussed during our high-power cyclotron workshop were the application of cyclotrons in particle physics, specifically neutrino physics, and as drivers for muon production. Furthermore, as these same accelerators have important applications in the fields of isotope production and possibly in energy research, we have included those topics as well. Finally, we took a look at Fixed Field Alternating Gradient accelerators (FFAs) and their potential to become high-intensity machines.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".