A next-generation RF linac as proton driver for CANS
Bibliographic record
Abstract
The use of neutrons is established since decades and essential for industry, medicine, life sciences, and research. Classical neutron sources are mainly neutron generators, with low neutron flux, or research reactors and spallation sources, which are large and cost intensive installations. A cost efficient, effective, and compact neutron source could bridge the gap existing and offer potential users either a dedicated standalone version for high demands of a single application or a full variable user facility. Such a compact accelerator-driven neutron source based on a radio frequency linear accelerator (linac) accelerating 10–20 mA of proton current to energies between 8 and10 MeV can deliver neutron fluxes between 1e9 and 1e13 n/cm 2 /s. A concept for a reliable proton linear accelerator using a combination of high duty cycle H-mode cavities that can be cooled well and 4-rod radio frequency quadrupoles suitable for cw operation presents a cost efficient, reliable, and well-proven linac design for such applications. The neutron target is based on diffusion bonded beryllium as the most suitable choice to be operated for such a neutron source. The linac will have about 15 m in total length including the target station and can be installed and operated for reasonable costs. We will present the current status of such an accelerator-based neutron source and potential perspectives.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".