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Beam-Based Measurements of the ISAC-II Superconducting Heavy Ion Linac

2018· article· en· W2890795013 on OpenAlex
Spencer Kiy, Robert Laxdal, M. Marchetto, Stephanie Rädel, Olivier Shelbaya

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJACOW · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsTRIUMF
Fundersnot available
KeywordsLinear particle acceleratorBeam (structure)Heavy ionPhysicsNuclear physicsNuclear engineeringIonEngineeringOptics

Abstract

fetched live from OpenAlex

Preparation for experiments, which typically run for one to two weeks in the ISAC-II facility at TRIUMF, requires some amount of overhead, limiting the efficiency of the facility. Efforts are underway to improve the ISAC-II linac model to reduce this overhead while also improving the quality of the delivered ion beam. This can be accomplished with beam-based measurements and corrections of alignment, cavity gradients, focal strengths, and more. A review of the present state of the linac will be given, including measured mis-alignments and other factors that affect the reproducibility of tunes. The outlook on expected improvements will also be summarized, including progress on the automatic phasing of cavities with a focus on integration to the High Level Application platform being developed at TRIUMF. Lastly, a summary will be given on the expected paradigm shift in the tuning approach taken: moving from re-active tuning by operators or beam delivery experts to pro-active measurements and investigations, version-controlled tunes, and continuous feedback from beam physicists.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.268

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 head0.039
GPT teacher head0.244
Teacher spread0.205 · 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