Bibliographic record
Abstract
The production and acceleration of secondary, radioactive ion beams differ quite substantially from the case of stable ion beams. The individual production and separation of the specific ion species, low beam currents or short lifetime of the ions as well as the needed energy variability, typically ranging from 1 to 8 MeV/u, have consequences on the layout of the accelerator. Long living isotopes can be bred to higher charge state, like it is done at the REX/ISOLDE project at CERN, and accelerated very efficiently with an accelerator similar to the CERN Lead Linac or the GSI High Charge State Injector. In case of short isotope lifetimes below a few milliseconds charge breeding is not feasible. Therefore, the ions produced in a low charge state have to be accepted by the accelerator. A linac designed for high mass to charge ratio, like the TRIUMF ISAC or the GSI High Current Injector with A/q < 65, which started routine operation in 1999, could serve this task. Due to the low intensities of some of the ion species a high duty cycle is required. Additionally, coincidence experiments profit a lot from a cw ion beam. This contribution will review the basic needs for radioactive beam acceleration and the possible solutions. It covers linacs based on RFQs, quarter wave resonators and H-mode cavities. Room temperature as well as superconducting solutions are discussed. The state of the art and future perspectives will be described.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".