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Record W4413275932 · doi:10.1139/cjp-2025-0094

Beam dynamics design for alternating phase focusing proton Linac for a compact accelerator-based neutron source

2025· article· en· W4413275932 on OpenAlexaffvenueabout
Mina Abbaslou, Robert Laxdal, Tobias Junginger, Philipp Kolb, M. Marchetto, O. Kester, Drew Marquardt

Bibliographic record

VenueCanadian Journal of Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsUniversity of WindsorUniversity of VictoriaTRIUMF
Fundersnot available
KeywordsPhysicsLinear particle acceleratorBeam (structure)ProtonDynamics (music)Nuclear physicsPhase (matter)NeutronNeutron sourceAccelerationParticle acceleratorOpticsClassical mechanics

Abstract

fetched live from OpenAlex

A prototype Canadian compact accelerator-driven neutron source is proposed for installation at the University of Windsor. The source is based on a high-intensity compact proton RF linear accelerator (Linac) that delivers an average current of 10 mA of protons at 10 MeV to the target. The accelerator consists of a short radio frequency quadrupole, followed by an efficient drift tube Linac (DTL) structure. This study compares the alternating phase focusing (APF) DTL with other DTL variants, such as Alvarez and Crossbar H-mode (CH) DTLs, using KONUS and negative synchronous phase beam dynamics. The APF-DTL design employs an RF phase variation for transverse and longitudinal beam focusing, avoiding magnetic lenses. A detailed optimization of the synchronous phases yielded a configuration that minimizes emittance growth, though the APF-DTL showed increased sensitivity to transverse emittance constraints. The results suggest that while APF-DTL offers operational simplicity, more standard DTL variants such as Alvarez and CH-KONUS provide better beam quality and power efficiency for high-intensity applications like CANS.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.541
Threshold uncertainty score0.815

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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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 teacher head, 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
Published2025
Admission routes3
Has abstractyes

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