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Designing a High-Performance Quarter Waveplate for Improved Signal Quality in X-ray Magnetic Circular Dichroism Studies at EMA Beamline

2025· article· en· W4411063389 on OpenAlexaboutno aff
G S de Albuquerque, M Saveri Silva, R D Resck, Tiago Nunes Soares, J V E Matoso, JULIO FURTADO, Clara Sanches Bueno, J P I Astolfo

Bibliographic record

VenueJournal of Physics Conference Series · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCrystallography and Radiation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsBeamlineWaveplateCircular dichroismMagnetic circular dichroismOpticsX-ray magnetic circular dichroismCircular polarizationSIGNAL (programming language)Quality (philosophy)Quarter (Canadian coin)PhysicsComputer scienceChemistryCrystallographyAstronomyBeam (structure)HistorySpectral line

Abstract

fetched live from OpenAlex

Abstract X-ray Magnetic Circular Dichroism (XMCD) is a key technique for probing the magnetic properties of intricate materials, utilizing differences in absorption spectra of circularly polarized X-rays with opposite helicities from a magnetized sample. At the EMA beamline in Sirius, polarization control is achieved through quarter-wave plates, enabling changes in X-ray helicity at a rate of 10 Hz. This work proposes an instrumentation redesign to increase the frequency of X-ray polarization switching, optimizing the use of EMA’s high photon flux and improving XMCD signal quality. The new design aims to oscillate crystals up to 200 Hz, operate between 3 and 22 keV, follow the high dynamic double crystal monochromator fly-scan at 1 keV / s, accommodate six different crystal configurations, and maintain a vacuum level better than 10 −8 mbar. This advance is expected to significantly enhance XMCD studies, particularly for samples with very low magnetization, and advance research within the synchrotron community by providing a versatile and high-performance tool for investigating magnetic properties.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.270
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 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
Published2025
Admission routes1
Has abstractyes

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