Signal enhancement strategies for angular profile measurements of gas injected in ultrahigh vacuum
Bibliographic record
Abstract
With current trends in physical chemistry and various ultrahigh vacuum (UHV) based fabrication techniques, such as thin film deposition and epitaxy, there is a growing need to develop a precise and reliable simulation platform to predict the angular distribution of gas molecules injected in vacuum through a nozzle of a given geometry. Such models need to be validated through a systematic experimental study that clearly isolates the contributions of each parameter. For this purpose, a test platform dedicated to the measurement of the molecular beam angular profiles produced by a nozzle in UHV has been designed and built. Its main features are discussed, especially regarding its ability to produce precise and reproducible data. In order to reduce the contribution from the molecules scattered by the chamber walls to the measured flux densities and thus achieve a better signal-to-background ratio, several strategies are considered and evaluated. In particular, an innovative approach based on a direction selection tube is presented with a quantitative evaluation of its effect on the measurements. Finally a rule of thumb is proposed for the choice of the tube’s dimensions allowing a maximum background reduction while keeping the impact on the signal as small as desired.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".