ATRAP antihydrogen experiments
Bibliographic record
Abstract
Abstract Antihydrogen (Hbar) was first produced at CERN in 1996. Over the past decade our ATRAP collaboration has made massive progress toward our goal of producing large numbers of cold Hbar atoms that will be captured in a magnetic gradient trap for precise comparison between the atomic spectra of matter and antimatter. The AD at CERN provides bunches of 3 × 107 low energy Pbars every 100 seconds. We capture and cool to 4 K, 0.1% of these in a cryogenic Penning trap. By stacking many bunches we are able to do experiments with 3 × 105 Pbars. ∼100 e+/sec from a 22Na radioactive source are captured and cooled in the trap, with 5 × 106 available experiments.We have developed 2 ways to make Hbar from these cold ingredients, namely 3‐body collisions, and 2‐stage Rydberg charge exchange. In the first case, Pbars are injected into a nested trap containing e+. Hbar is formed when 2 e+ and 1 Pbar collide. In 2‐stage Rydberg charge exchange, laser‐excited caesium (Cs) enters the trap through a small hole. Rydberg positronium is formed when a e+ captures an e‐from a Cs. These atoms exit the trap, some passing through a nearby cloud of cold Pbars. A 2nd charge‐exchange results when a Pbar captures the e+, forming Hbar. We have also developed techniques to measure the excited‐state distribution of the Hbar and measure their velocity. I will present results from these experiments and discuss the next generation of apparatus to be commissioned this year. This new apparatus includes a e+ accumulator built at York University providing many more e+. The new Pbar annihilation detector provides spatial information of annihilations. Windows allow lasers to enter the trap for spectroscopic measurements and for laser cooling of the Hbar. Possibly the most exciting inclusion in this new apparatus is the inclusion of a neutral particle trap which may, for the first time, capture the Hbar and lead to the first atomic spectrum from antimatter. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".