Simulations of a cryogenic, buffer-gas filled Paul trap for low-emittance ion bunches
Bibliographic record
Abstract
Many experiments with pulsed ion beams benefit from or even require ion bunches with both small temporal width as well as small energy spread. To achieve optimal ion-beam preparation, a buffer-gas filled cryogenic Paul trap is being developed in the context of the Multi Ion Reflection Apparatus for Collinear Laser Spectroscopy (MIRACLS). There, ion bunches of short-lived radionuclides are trapped in a Multi-Reflection Time-of-Flight (MR-ToF) device. Thus, the ions can be repeatedly probed by a laser beam compared to only once in conventional, single-passage collinear laser spectroscopy. To fulfill MIRACLS’ opposing requirements of a small temporal ion-bunch width and small energy spread, a buffer-gas filled cryogenic Paul trap is envisioned. Ion-optical simulations confirm the advantages of cryogenic temperatures and the linear scaling of the beam emittance as a function of the buffer-gas temperature. Beyond MIRACLS, high-quality ion beams from a cryogenic Paul trap will be beneficial for other precision experiments at radioactive ion beam facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".