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Antiparticle sources for antihydrogen production and trapping

2011· article· en· W1994936225 on OpenAlexaff
M. Charlton, Gorm Bruun Andresen, M. D. Ashkezari, M. Baquero-Ruiz, W. Bertsche, P. D. Bowe, C. C. Bray, E. Butler, C. L. Cesar, S. C. Chapman, J. Fajans, T. Friesen, M. Fujiwara, D. R. Gill, J. S. Hangst, W. N. Hardy, R. Hayano, M. E. Hayden, A. J. Humphries, R. Hydomako, S. Jonsell, L. V. Jørgensen, Sean Kerrigan, L. L. Kurchaninov, R. Lambo, N. Madsen, S. Menary, P. Nolan, K. Olchanski, A. Povilus, P. Pusa, F. Robicheaux, E. Sarid, S. Seif El Nasr, D. M. Silveira, C. So, James Storey, R. I. Thompson, D. P. van der Werf, D. Wilding, J. S. Wurtele, Y. Yamazaki

Bibliographic record

VenueJournal of Physics Conference Series · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsUniversity of British ColumbiaYork UniversityTRIUMFUniversity of CalgarySimon Fraser University
FundersEngineering and Physical Sciences Research Council
KeywordsAntihydrogenAntiprotonPhysicsNuclear physicsAntiparticleLarge Hadron ColliderContext (archaeology)AntimatterTrappingCPT symmetryParticle physicsAtomic physicsPositronElectronProtonGeography

Abstract

fetched live from OpenAlex

Sources of positrons and antiprotons that are currently used for the formation of antihydrogen with low kinetic energies are reviewed, mostly in the context of the ALPHA collaboration and its predecessor ATHENA. The experiments were undertaken at the Antiproton Decelerator facility, which is located at CERN. Operations performed on the clouds of antiparticles to facilitate their mixing to produce antihydrogen are described. These include accumulation, cooling and manipulation. The formation of antihydrogen and some of the characteristics of the anti-atoms that are created are discussed. Prospects for trapping antihydrogen in a magnetic minimum trap, as envisaged by the ALPHA collaboration, are reviewed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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 teacher head, 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

Citations2
Published2011
Admission routes1
Has abstractyes

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