Adiabatic expansion cooling of antihydrogen
Bibliographic record
Abstract
Magnetically trapped antihydrogen atoms can be cooled by expanding the volume of the trap in which they are confined. We report a proof-of-principle experiment in which antiatoms are deliberately released from expanded and static traps. Antiatoms escape at an average trap depth of 0.08 ± 0.01 K (statistical errors only) from the expanded trap while they escape at average depths of 0.22 ± 0.01 and 0.17 ± 0.01 K from two different static traps. (We employ temperature-equivalent energy units.) Detailed simulations qualitatively agree with the escape times measured in the experiment and show a decrease of 38 % (statistical error < 0.2 % ) in the mean energy of the population after the trap expansion without significantly increasing antiatom loss compared to typical static confinement protocols. This change is bracketed by the predictions of one-dimensional and three-dimensional semianalytic adiabatic expansion models. These experimental, simulational, and model results are consistent with obtaining an adiabatically cooled population of antihydrogen atoms that partially exchanged energy between axial and transverse degrees of freedom during the trap expansion. This result is important for future antihydrogen gravitational experiments which rely on adiabatic cooling, and it will enable antihydrogen cooling beyond the fundamental limits of laser cooling. Published by the American Physical Society 2024
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".