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Record W2275148679 · doi:10.1088/0953-4075/49/6/064002

Large numbers of cold positronium atoms created in laser-selected Rydberg states using resonant charge exchange

2016· article· en· W2275148679 on OpenAlexaff
Robert McConnell, G. Gabrielse, W. Steven Kolthammer, Philip Richerme, A. Müllers, J. Walz, D. Grzonka, M. Zieliński, D. W. Fitzakerley, M. C. George, E. A. Hessels, C. H. Storry, M. Weel

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsYork University
Fundersnot available
KeywordsPositroniumAtomic physicsRydberg formulaCharge exchangeCharge (physics)PhysicsRydberg atomLaserIonizationNuclear physicsIonQuantum mechanicsPositronElectron

Abstract

fetched live from OpenAlex

Abstract Lasers are used to control the production of highly excited positronium atoms (Ps*). The laser light excites Cs atoms to Rydberg states that have a large cross section for resonant charge-exchange collisions with cold trapped positrons. For each trial with 30 million trapped positrons, more than 700 000 of the created Ps* have trajectories near the axis of the apparatus, and are detected using Stark ionization. This number of Ps* is 500 times higher than realized in an earlier proof-of-principle demonstration (2004 Phys. Lett. B 597 257). A second charge exchange of these near-axis Ps* with trapped antiprotons could be used to produce cold antihydrogen, and this antihydrogen production is expected to be increased by a similar factor.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.236
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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