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Coupling resonance correction and avoidance for the TRIUMF 500 MeV cyclotron

2023· article· en· W4324046212 on OpenAlexaff
Yi‐Nong Rao, Lige Zhang

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsPhysicsResonance (particle physics)CyclotronBetatronCoupling (piping)Cyclotron resonanceBeam (structure)TrimOscillation (cell signaling)Nuclear magnetic resonanceHarmonicAtomic physicsComputational physicsNuclear physicsOpticsMaterials scienceAcousticsComputer scienceChemistry

Abstract

fetched live from OpenAlex

Abstract The linear coupling resonance ν r - ν z = 1 in a cyclotron is driven by the first harmonic in the radial gradient of the radial magnetic field. In the TRIUMF 500 MeV cyclotron, this resonance is encountered multiple times. When the circulating beam is off-centred radially passing through the resonance, the radial betatron oscillation can be converted into vertical oscillation, which can cause beam losses and radio-activation. We investigated this resonance with goal to correct it by using the available harmonic correction coils. Moreover, we improved the cyclotron vertical tune measurement by using trim coils to create a flat-top radial field, and thus confirmed an extra ν r - ν z = 1 coupling resonance passage as this is unexpected from the historical tune diagram. To avoid this passage, the local vertical tune is adjusted to stay farther away from the resonance line by using the trim coils axial field, but at the cost of a local excursion in isochronism. After the correction and the avoidance of this resonance, both the coherent and incoherent vertical oscillations are decreased, thus helping to reduce the machine tank spills under high intensity operation. In this paper, we present the results of calculations and simulations as well as measurements that we undertook.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.139

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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 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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