Pinhole aperture point backlighter development experiments on Trident, 9-13, 2001
Bibliographic record
Abstract
Pinhole aperture point backlighter (PAPBL) imaging has been used on experiments on Omega, but results have been compromised by large backgrounds. This technique has advantages over traditional area backlighting/pinhole imaging, and the Omega experiments could benefit from this capability, but Omega time is expensive and not the place for developing diagnostic techniques if they can be developed on Trident instead. PAPBL, shot from Direct Drive Cylinder Mix experiments on Omega (DDCYLMIX 00-1, January 18 and 19, 2000). [See LA-UR-00-4187, Post-Shot Report, Direct Drive Cylinder Mix]. In this campaign, they used Trident to obtain clean PAPBL images. Having accomplished that, they attempted to replicate the noise environment of Omega by producing hot electrons and having them impinge on material to produce high-energy x-rays similar to those that might be produced by hot electrons impinging on diagnostics or target positioner components on Omega. Backlighter target design was based, to some degree, on that shown by Bullock et al. at the 42nd Annual APS-DPP Meeting in Quebec City, Quebec, Canada, October 23-27, 2000. [A.B. Bullock et al., Bull. Am. Phys. Soc. 45,(7) 359 (2000); A.B. Bullock et al., Rev. Sci. Instrum. 72, 690 (2001).] We accomplished this to some degree and then attempted, with some success, to obtain a good PAPBL image in the presence of this noise. Results of this work suggest methods that might reduce the background noise in Omega PAPBL images. The goals are to obtain a pinhole aperture point backlighter (PAPBL) image on Trident and develop a method to simulate the high-energy background contribution to PAPBL imnages seen on Omega experients in order to allow future experiments to optimize signal-to-noise in PAPBL imaging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".