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Benchmarking of CST and Trak in the simulation of an electron gun for a future C<sup>6+</sup> ion source

2025· article· en· W4407965003 on OpenAlexfundno aff
J. R. Etxebarria, Gabriela Moreno, D. Obradors, C. Oliver, A. Pikin, Charles J. Battaglia, J. M. Carmona, A. Tato

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsnot available
FundersEuropean Regional Development FundOtsuka Canada PharmaceuticalCentro para el Desarrollo Tecnológico IndustrialEuropean Commission
KeywordsBenchmarkingIonElectron gunElectronPhysicsAtomic physicsNuclear physicsComputer scienceCathode rayQuantum mechanicsBusiness

Abstract

fetched live from OpenAlex

Abstract This study benchmarks the CST Studio Suite and Trak codes in simulating an electron gun for a future C6+ hadrontherapy installation. CST Studio Suite offers 3D simulation capabilities, allowing detailed modelling of complex geometries and electromagnetic fields. Trak, on the other hand, offers efficient 2D simulations, which can significantly reduce computational time while still providing valuable results. These studies are being conducted in the framework of a project dedicated to the design of the initial stage of a C6+ hadrontherapy linac. The electron gun studied is based on the MEDeGUN developed at CERN. This study provides an overview of the results obtained so far and outlines plans for future improvements and development.

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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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