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Intensity limit in compact H<sup>-</sup> and H<sup>+</sup> <sub>2</sub> cyclotrons

2023· article· en· W4324046137 on OpenAlexaff
Thomas Planche, R. Baartman, Hui Wen Koay, Yi‐Nong Rao, Lige Zhang

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of VictoriaTRIUMF
Fundersnot available
KeywordsPhysicsCyclotronNuclear physicsScalingAtomic physicsElectron

Abstract

fetched live from OpenAlex

Abstract Compact H- cyclotrons are used all across the globe to produce medical isotopes. Machines with external ion sources have demonstrated average extracted currents on the order of a few mA, although reported operational numbers are typically around 1 mA or below. To explore the possibility of extracting even more current from such cyclotrons, it is important to understand the mechanisms that drive intensity limits and how they scale. In this paper we review some of the key aspects of the beam dynamics in the central region of compact cyclotrons, including rf electric focusing and space charge effects. We derive the scaling of the phase acceptance with the rf gap voltage, harmonic number, etc. We also explore the scaling with different types of ions such as H-, H+ 2 and H+ 3. We discuss the impact of mechanical erosion of the central region electrodes. Thoughout the paper, we use examples and experimental data from two compact H- cyclotrons for reference: the TR-30 series and the TRIUMF 500 MeV machine.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.237
Teacher spread0.222 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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