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Record W4407371306 · doi:10.1063/5.0233169

Bayesian optimization for ion beam centroid correction

2025· article· en· W4407371306 on OpenAlex
E. Ghelfi, A. Katrusiak, R. Baartman, W. Fedorko, O. Kester, G. Kogler Anele, Olivier Shelbaya, D. Tanyer

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReview of Scientific Instruments · 2025
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaQueen's UniversityUniversity of WaterlooTRIUMF
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCentroidBeam steeringComputer scienceOverhead (engineering)Beam (structure)Bayesian optimizationIon beamBayesian probabilitySet (abstract data type)PhysicsOpticsArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

An activity of the TRIUMF automatic beam tuning program, the Bayesian optimization for Ion Steering (BOIS) method has been developed to perform corrective centroid steering of beams at the TRIUMF ISAC facility. BOIS exclusively controls the steerers for centroid correction after the transverse optics have been set according to theory. The method is fully online, easy to deploy, and has been tested in low energy and post-accelerated beams at ISAC, achieving results comparable to human operators. scaleBOIS and boundBOIS are naïve proof-of-concept solutions to preferably select beam paths with minimal steering. Repeatable and robust automated steering reduces reliance on operator expertise and operational overhead, ensuring reliable beam delivery to the experiments and thereby supporting TRIUMF's scientific mission.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.311

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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