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Improved Simulation for Centre Region of TRIUMF 500 MeV Cyclotron with Space Charge

2018· article· en· W3186228688 on OpenAlexaff
Yi‐Nong Rao, R. Baartman, Thomas Planche

Bibliographic record

VenueJACOW · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsCyclotronNuclear physicsPhysicsCharge (physics)Space chargeParticle physicsElectron

Abstract

fetched live from OpenAlex

The TRIUMF 500 MeV cyclotron delivered routinely a total current up to 200 μA protons for 15 years till 2001. Since 2002, developments towards 300 μA total extraction became compelling because of the ISAC expansion. To meet future requirements (for addition of a new beam-line), a total extraction of 310 − 450 μA shall be envisioned. With such an increase of beam current, the space charge effect becomes a major concern in the centre region, as it limits the maximum amount of beam current achievable out of the machine. Therefore, numerical simulation on beam orbits with the space charge force has has been initiated, starting from the injection gap. This study is focused on the beam bunches which are very long compared with transverse size (because TRIUMF extraction is by stripping of H-minus and separated turns are not required). In order to achieve an improved understanding of the space charge effect, we worked to validate the simulations performed without and with the space charge force, using realistic centre region geometry. Our goal is to work out the space charge limits and their dependence upon the bunchers, rf voltage, and matching. In this paper we present our recent progress in this study.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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