Ray tracing based performance optimization of an x-ray beamline that incorporates a sagittally curved mirror
Bibliographic record
Abstract
X-ray mirrors with sagittal curvature are often used at X-ray beamlines to focus or collimate the beam horizontally. However, if placed in front of a monochromator, the vertical beam divergence introduced by such mirrors can significantly decrease energy resolution and make the beamline more sensitive to misalignment. Such is the case of BioXAS-Side beamline at the Canadian Light Source Inc. To find a cost effective way to minimize the negative effects of a toroidal first mirror while preserving flu x, we used the ray tracing package XRT to simulate the beamline, understand the cause of the observed problems, and investigate ways to improve performance. We confirmed that the optimal solution would be to limit the horizontal acceptance of the toroidal mirror, as commonly done, because the sagittal curvature of the mirror contributes to the vertical deflection of the light rays proportionally to their transversal coordinate. However, that was not an option in our case given the high cost of white-beam slits and space limitations specific to this project. Instead we used a v-shaped slit limiting the beam horizontally right after the monochromator, where rays with very different energies are still not merged together. The v-slit was cheaper to manufacture and install than a white beam slit in front of the mirror, while it improved energy resolution and decreased its dependence on beamline alignment equally well. The procedure we detail here can be generalized and used to design or improve similar systems, especially when mirrors with sagittal curvature are involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".