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Improvement of the Efficiency and Beam Quality of the TRIUMF Charge State Booster

2022· article· en· W4224680737 on OpenAlexaff
J Adegun, F. Ames, O. Kester

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsSaint Mary's UniversityUniversity of VictoriaTRIUMF
Fundersnot available
KeywordsBooster (rocketry)Thermal emittanceIon sourceCyclotronBeam (structure)Atomic physicsIon beamBeam emittanceMaterials scienceSeparator (oil production)QuadrupoleIonNuclear physicsPhysicsOpticsElectronPlasma

Abstract

fetched live from OpenAlex

Abstract An Electron Cyclotron Resonance Ion Source is used as the charge state booster (CSB) at the Isotope Separator and Accelerator facility (ISAC) of TRIUMF. Since its commissioning in 2010, the source has been used to charge breed radioactive ions ranging from potassium to erbium operating in single-frequency heating mode. Under this regime, the single charge state efficiency of the booster was measured up to 6 % for noble gases and the maximum charge state of Cesium that can be measured is 27+. The rf system of the source was recently upgraded to implement the two-frequency heating using a single waveguide. Preliminary operation of the booster in a two-frequency heating mode shifted the maximum charge state of Cesium that can be measured to 30+. Another point of improvement that is being addressed is the beam extraction system of the booster. A quadrupole scan technique using a thick lens approach has been developed to measure the emittance of the extracted beam and to analyze the quality of the extraction system. Overall, the system of the CSB is currently being optimized in preparation for the detailed determination of the effect of the two-frequency heating on the beam output.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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