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Design and performance of a novel low energy multispecies beamline for an antihydrogen experiment

2023· article· en· W4366771097 on OpenAlexafffund
C. J. Baker, W. Bertsche, A. Capra, C. L. Cesar, M. Charlton, A. Christensen, R. Collister, A. Cridland Mathad, S. Eriksson, A. Evans, N. Evetts, Sara Fabbri, J. Fajans, T. Friesen, M. Fujiwara, D. R. Gill, P. Grandemange, P. Granum, J. S. Hangst, M. E. Hayden, D. Hodgkinson, C. A. Isaac, M. A. Johnson, J. M. Jones, Stephen A. Jones, Alexander Khramov, L. L. Kurchaninov, N. Madsen, D. Maxwell, J. T. K. McKenna, S. Menary, Takamasa Momose, P. S. Mullan, J. J. Munich, K. Olchanski, J. Peszka, A.D. Powell., C. Ø. Rasmussen, R. L. Sacramento, M. Sameed, E. Sarid, D. M. Silveira, C. So, Darij Markian Starko, G. Stutter, T. D. Tharp, R. I. Thompson, C. Torkzaban, D. P. van der Werf, J. S. Wurtele

Bibliographic record

VenuePhysical Review Accelerators and Beams · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityUniversity of CalgaryYork UniversityTRIUMF
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaLeverhulme TrustFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroIsrael Science FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoNational Science FoundationTRIUMFRoyal SocietyEngineering and Physical Sciences Research CouncilCERNU.S. Department of Energy
KeywordsAntihydrogenBeamlineNuclear physicsPhysicsEnergy (signal processing)Nuclear engineeringPositronEngineeringOpticsBeam (structure)AntimatterElectron

Abstract

fetched live from OpenAlex

The ALPHA Collaboration, based at the CERN Antiproton Decelerator, has recently implemented a novel beamline for low energy (≲100 eV) positron and antiproton transport between cylindrical Penning traps that have strong axial magnetic fields.Here, we describe how a combination of semianalytical and numerical calculations was used to optimize the layout and design of this beamline.Using experimental measurements taken during the initial commissioning of the instrument, we evaluate its performance and validate the models used for its development.By combining data from a range of sources, we show that the beamline has a high transfer efficiency and estimate that the percentage of particles captured in the experiments from each bunch is ð78 Æ 3Þ% for up to 10 5 antiprotons and ð71 Æ 5Þ% for bunches of up to 10 7 positrons.

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)0.003
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 score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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