New Source of Dense, Cryogenic Positron Plasmas
Bibliographic record
Abstract
We have developed a new method, based on the ballistic transfer of preaccumulated plasmas, to obtain large and dense positron plasmas in a cryogenic environment. The method involves transferring plasmas emanating from a region with a low magnetic field (0.14 T) and relatively high pressure (${10}^{\ensuremath{-}9}\text{ }\text{ }\mathrm{mbar}$) into a 15 K Penning-Malmberg trap immersed in a 3 T magnetic field with a base pressure better than ${10}^{\ensuremath{-}13}\text{ }\text{ }\mathrm{mbar}$. The achieved positron accumulation rate in the high field cryogenic trap is more than one and a half orders of magnitude higher than the previous most efficient UHV compatible scheme. Subsequent stacking resulted in a plasma containing more than $1.2\ifmmode\times\else\texttimes\fi{}{10}^{9}$ positrons, which is a factor 4 higher than previously reported. Using a rotating wall electric field, plasmas containing about $20\ifmmode\times\else\texttimes\fi{}{10}^{6}$ positrons were compressed to a density of $2.6\ifmmode\times\else\texttimes\fi{}{10}^{10}\text{ }\text{ }{\mathrm{cm}}^{\ensuremath{-}3}$. This is a factor of 6 improvement over earlier measurements.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".