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Evaluation of Immutability against Radiation-induced Demagnetization for a Hybrid Wiggler with NdFeB Magnets at the Canadian Light Source

2016· article· en· W2558878844 on OpenAlexaffabout
Cameron Baribeau, Drew Bertwistle, Les Dallin, J. Vogt, Ward Wurtz

Bibliographic record

VenueJACOW · 2016
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsChicken Farmers of Saskatchewan (Canada)
Fundersnot available
KeywordsNeodymium magnetMagnetImmutabilityWigglerDemagnetizing fieldScramMaterials scienceLight sourceOpticsEngineeringNuclear engineeringPhysicsMechanical engineeringNuclear physicsComputer scienceMagnetization

Abstract

fetched live from OpenAlex

The BioXAS beamline at Canadian Light Source installed a hybrid wiggler in 2013. Quantitative studies building on the experience of other facilities suggest the wiggler's NdFeB magnets are at risk of demagnetization due to radiation induced by the synchrotron's 2.9 GeV electrons. We use a phenomenological model to convert simulated peak demagnetizing fields into a radiation dose corresponding to a chosen %-demagnetization, and compare against an estimated dose per year due to injected beam. We find that injecting with the wiggler closed will cause 1% demagnetization in sections of its magnet blocks within 2 years of operation, assuming a worst case scenario. The wiggler has thus far been forced open for injections, but this will cease to be an option when CLS moves to top-up operation. In a related test, qualitative measurements of radiation during injections with the wiggler closed were taken by covering its magnets in Polaroid film. We find that radiation drops significantly when the injection efficiency is well-tuned. Our results suggest the wiggler will not receive damaging levels of radiation at closed gap so long as the injection system remains optimized.

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)0.001
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 score0.107
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.225
Teacher spread0.206 · 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 designBench or experimental
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
Published2016
Admission routes2
Has abstractyes

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