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Record W4388195782 · doi:10.1139/cjp-2023-0078

A dedicated application of evolutionary algorithms: synchrotron X-ray radiation optimization based on an in-vacuum undulator

2023· article· en· W4388195782 on OpenAlexvenueno aff
Bora Ketenoğlu, Erkan Bostancı, Didem Ketenoğlu, Ali Can Canbay, Manuel Harder, Adnan Sahin Karaca, Engin Eren, Ayhan Aydın, Zhong Yin, Mehmet Serdar Güzel, M. Martins

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsUndulatorBeamlinePhysicsSynchrotron radiationSynchrotronOpticsPhotonHigh-energy X-raysAdvanced Photon SourceParticle acceleratorBrightnessLinear particle acceleratorPhoton energySPring-8Beam (structure)

Abstract

fetched live from OpenAlex

Following the rapid growth in accelerator-based light sources research since the mid of 20th century, miscellaneous third generation synchrotron radiation (SR) facilities such as SSRL, APS, ESRF, PETRA-III, and SPring-8 have come into existence. These SR source facilities provide 10 20 –10 25 photons/s/mrad 2 /mm 2 /0.1%BW peak brightness within the photon energy range of 10–10 5 eV. Since different measurement techniques are utilized at X-ray beamlines of SR facilities, many kinds of insertion devices (i.e., undulators and wigglers) and optical components (e.g., high-resolution monochromators, double-crystal monochromators, lenses, mirrors, etc.) are employed for each experimental setup as a matter of course. Under the circumstances, optimization of a synchrotron beamline is a big concern for many scientists to ensure required radiation characteristics (i.e., photon flux, spot size, photon energy, etc.) for dedicated user experiments. In this respect, an in-vacuum hybrid undulator driven by a 6 GeV synchrotron electron beam is optimized using evolutionary algorithms (EA). Finally, it is shown that EA results are well consistent with both the literature and the analytical calculations, resulting in a promising design estimation for beamline scientists.

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 score0.936
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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