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Record W1490393906 · doi:10.1080/08940886.2015.1037673

Cryogenic Permanent Magnet Undulator Development at HZB/BESSY II

2015· article· en· W1490393906 on OpenAlexaboutno aff
J. Bahrdt, Carsten Kuhn

Bibliographic record

VenueSynchrotron Radiation News · 2015
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsnot available
Fundersnot available
KeywordsUndulatorRemanencePhysicsMagnetCoercivitySynchrotron radiationOpticsEngineering physicsCondensed matter physicsRadiationMagnetizationMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

The costs of a synchrotron radiation facility scales approximately linearly with the length (FEL) or the circumference (storage ring) of the machine. It is always beneficial for the reduction in overall expenses to utilize short period in-vacuum undulators (IVUs) for X-ray production. This is the reason for the success of the IVU development which was started almost 20 years ago in Japan [1 T. Hara, J. Synch. Rad., 5, 403–405 (1998).[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]–3 T. Tanaka, Proc. FEL Conf., Stanford, CA, 370–377 (2005). [Google Scholar]]. Today, IVUs are implemented into nearly all third-generation storage rings. Ten years ago, the concept of cryogenically cooled permanent magnet undulators (CPMUs) was proposed [4 T. Hara, Phys. Rev. ST Accel. Beams, 7, 050702-1-6 (2004).[Crossref] , [Google Scholar]]. The magnetic properties of rare earth magnets (i.e., the remanence and the coercivity) improve substantially at low temperatures. The remanence increases by about 15%, whereas the coercivity grows by a factor of three to four. Due to the performance gain and the low technical risk of CPMUs, such devices are under development all over the world. The first generation of CPMUs, with period lengths well below 20 mm, is successfully operated at ESRF [5 J. Chavanne, AIP Conf. Proc., SRI 2009, Melbourne, Australia 1234, 25–28 (2010). [Google Scholar], 6 J. Chavanne, Proc. PAC, Vancouver, BC, Canada, 2414–2416 (2009). [Google Scholar]], PSI [7 T. Tanaka et al, Phys. Rev. ST Accel. Beams, 12, 120702-1-5 (2009).[Crossref] , [Google Scholar]], DIAMOND [8 C. Ostenfeld and M. Pedersen, Proc. IPAC, Kyoto, Japan, 3093–3095 (2010). [Google Scholar]], SOLEIL [9 C. Benabderrahmane, J. Phys., Conf. Ser., SRI 2012, Lyon, France 425, 032019-1-4 (2013). [Google Scholar]], and SPring-8 [10 T. Tanaka, Private communication (2015). [Google Scholar]].

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.017
GPT teacher head0.216
Teacher spread0.200 · 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 designNot applicable
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

Citations13
Published2015
Admission routes1
Has abstractyes

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