Design of a High Performance Soft X-Ray Emission Spectrometer for the REIXS Beamline at the Canadian Light Source
Bibliographic record
Abstract
The optical design of a soft X-ray (90-1100~eV) emission spectrometer for the Resonant Elastic and Inelastic X-ray Scattering (REIXS) beamline to be implemented at the CLS is presented. An overview of soft X-ray optical theory as it relates to diffraction gratings is given. The initial constraints and the process that led to this design are outlined. Techniques and software tools that were developed, using ray-tracing and diffraction grating efficiency calculations, are discussed. The analysis completed with these tools to compare existing soft X-ray emission spectrometer designs is presented. Based on this analysis, a new design with superior performance for this application is proposed and reviewed. This design employs Rowland circle geometry to achieve a resolving power in excess of 2,500 in the range of interest. In addition, a novel design is proposed for a larger extremely high resolution spectrometer which will provide resolving powers exceeding 10,000 throughout the higher end of this range. A review is given of research into the components, manufacturing techniques and tolerances that will be required to produce this spectrometer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".