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Record W6910625220 · doi:10.48448/4c2n-xm73

Design and Simulation of Transparent Injection Upgrade for the CLS Storage Ring

2021· other· en· W6910625220 on OpenAlexaffabout

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStorage ringUpgradeSynchrotronMagnetBooster (rocketry)Particle acceleratorInjectorSynchrotron light source

Abstract

fetched live from OpenAlex

The Canadian Light Source (CLS) synchrotron uses four fast kicker magnets to inject electrons into the storage ring from a 2.9 GeV booster ring. The injection occurs over several turns of the stored beam, which is also perturbed by the injection kickers. The resultant oscillations of the stored beam can negatively affect the quality of beamline experiments, so it is desirable to implement an injection scheme which does not disturb the stored beam. Injection schemes of this type allow for transparent injection and are very desirable for the planned top-up operations of the CLS storage ring. Many alternative injection techniques have been presented in recent years and we have examined several of these techniques as they apply to the CLS storage ring. Pulsed multipole magnets and a non-linear kicker are the most viable alternatives for integration with the current ring. Non-linear kicker designs are also being considered for the proposed CLS 2.0 and studying this injection method in the limitations of the current machine provides additional insight to guide the work on the new machine. Simulation with the accelerator code ELEGANT shows the viability of the non-linear kicker design as developed at BESSY, MAX IV and SOLEIL (other synchrotron facilities) for transparent injection at the CLS.

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: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.097
GPT teacher head0.350
Teacher spread0.253 · 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
GenreMethods

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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same venueUnderline Science Inc.French-language works237,207