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Record W2060168586 · doi:10.1063/1.1699454

Numerical investigation for high intensity H− beam injection to a 100 MeV compact cyclotron

2004· article· en· W2060168586 on OpenAlexfundno aff
Tianjue Zhang, Hongjuan Yao, Xialing Guan, Chengjie Chu, Junqing Zhong, Zhiguo Yin

Bibliographic record

VenueReview of Scientific Instruments · 2004
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaTRIUMF
KeywordsCyclotronPhysicsSolenoidBeam (structure)ProtonNuclear physicsUpgradeBeamlineIntensity (physics)OpticsAtomic physicsComputer sciencePlasma

Abstract

fetched live from OpenAlex

As a part of the Upgrade Project of Beijing Tandem Accelerator Laboratory, a 100 MeV compact cyclotron was designed for the generation of high intensity proton beam. In comparison the H− beam intensity injected into the cyclotron central region of the 30 MeV medical cyclotron developed at CIAE 8 years ago, those injected into the 100 MeV machine will be 4 times higher. So, the axial injection optics was investigated numerically again by means of taking the space charge effect into account. The simulation started from the old layout based on the ES (Einzell lens and solenoid) system in CIAE’s 30 MeV machine and SQQ system (solenoid and doublet) in TRIUMF’s machine, shows that a ESQQ system should be able to match the injection optics better for higher intensity beam injection. A new layout based on ESQQ injection system will be used for the 100 MeV cyclotron. From three-dimensional field computation, the modularization of magnets (S and QQ) are designed so that the injection line ESQQ could be rearranged flexibly.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.263
Teacher spread0.240 · 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
Published2004
Admission routes1
Has abstractyes

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