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Strongly curved super-conducting magnets: beam optics modeling and field quality

2023· article· en· W6906731129 on OpenAlexaff

Bibliographic record

VenueJACOW · 2023
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsInternational Institute for Sustainable Development
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsMagnetAperture (computer memory)CurvatureBeam (structure)Geometrical opticsLaser beam qualityField (mathematics)Transverse planeSynchrotron

Abstract

fetched live from OpenAlex

Superconducting dipoles with a strong curvature (radius smaller than 2 meters, for an aperture of about 100 mm and a length of 1-3 meters) are required for applications where compactness is key, such as the synchrotron and gantry for Carbon-ion therapy developed within the European program HITRIplus. Such magnets challenge several assumptions in the field description and put to the test the range of validity of beam optics codes. In particular, the equivalence that holds for the straight magnets between the transverse multipoles description obtained from the Fourier analysis (used for magnet design and measurements) and the Taylor expansion of the vertical field component along the horizontal axis (used in beam optics) is not valid any longer. A proper fringe field modelling also becomes important, due to the curved geometry and the aperture being large compared to the magnetic length. We explore the feasibility and the limits of modeling such magnets with optics elements (such as sector bends and multipoles), which allows parametric optics studies for optimization, field quality definition and fast long-term multi-pass tracking.

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: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.509

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 head0.068
GPT teacher head0.287
Teacher spread0.219 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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