A design methodology using signal‐to‐noise ratio for plastic scintillation detectors design and performance optimization
Bibliographic record
Abstract
PURPOSE: The design of novel plastic scintillation detectors (PSDs) is impeded by the lack of a suitable framework to simulate and predict their performance. The authors propose to use the signal-to-noise ratio (SNR) to model the performance of PSDs that use charge-coupled devices (CCDs) as photodetectors. METHODS: In PSDs using CCDs, the SNR is inversely related to the normalized standard deviation of the dose measurement. Thus, optimizing the SNR directly optimizes the system's precision. In this work, a model of SNR as a function of the system parameters is derived for optical fiber-based PSD systems. Furthermore, this proposed model is validated using experimental results. A formula for the efficiency of fiber coupling to CCDs is derived and used to simulate the performance of a PSD under varying magnifications. RESULTS: The proposed model is shown to simulate the experimental performance of an actual PSD to a suitable degree of accuracy under various conditions. CONCLUSIONS: The SNR constitutes a useful tool to simulate the dosimetric precision of PSDs. Using the SNR model, recommendations for the design and optimization of PSDs are provided. Using the same framework, recommendations for non-fiber-based PSDs are also provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".