Kirkpatrick-Baez microscope for hard X-ray imaging of fast ignition experiments
Bibliographic record
Abstract
A Kirkpatrick-Baez X-ray microscope has been developed for use on the Titan laser facility at the Lawrence Livermore National Laboratory in Fast Ignition experiments. It was developed as a broadband alternative to narrow band Bragg crystal imagers for imaging Kα emission from tracer layers. A re-entrant design is employed which allows for alignment from outside the chamber. The mirrors are coated with Pt and operate at a grazing incident angle of 0.5° providing higher resolution than an equal brightness pinhole and sufficient bandwidth to image thermally shifted characteristic Kα emission from heated Cu tracer layers in Fast Ignition experiments. The superpolished substrates (<1 Å rms roughness) had a final visible wavelength roughness of 1.7 Å after coating, and exhibited a reflectivity corresponding to an X-ray wavelength roughness of 7 ± 1 Å. A unique feature of this design is that during experiments, the unfiltered direct signal along with the one-dimensional reflections are retained on the detector in order to enable a live indication of alignment and incident angle. The broad spectral window from 4 to 9 keV enables simultaneous observation of emission from several spectral regions of interest, which has been demonstrated to be particularly useful for cone-wire targets. An experimentally measured resolution of 15 μm has been obtained at the center of the field of view.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".