SU‐GG‐T‐349: Generalized Formalism and Optimization of the Calibration Sequence of Plastic Scintillation Detectors
Bibliographic record
Abstract
Purpose: Effects of Cerenkov light on plastic scintillation detectors (PSD) can be successfully corrected, but an inadequate calibration might result in erroneous measurements. The purpose of this work is to establish a general calibration formalism and to perform a systematic study of calibration sequences to optimize PSD performances. Methods and Materials: A general mathematical formalism has been developed with no a priori assumption on the physical characteristics of the detector system. This formalism was used in simulations of realistic PSD systems (i.e. similar to PSDs published in the literature) for over 1000 different calibration sequences. The accuracy and precision of dose measurements with PSD calibrated with each sequence were then analyzed and compared. We also studied the propagation of errors in the whole measurement chain. Results: Optimized calibration sequence can improve precision by a factor of 3 when dose readings are performed with more than 15 cm of optical fibers irradiated. In addition, a 3‐point calibration sequence prevents systematic error in calibration factors measured in the presence of large (>0.5%) statistical noise. Our study also outlined several “calibration pitfalls” that should be avoided: Calibration points that are too similar will unduly deteriorate measurement precision (e.g. calibration with fields of 10 and 30 cm will increase the measurement standard deviation by 1.5 compared to calibrations with fields of 3 and 30 cm); Small dosimetric errors made at calibration time can result in large systematic measurement error with PSDs. A 1% dose error on one of the calibration measurements can result in a 4% to 10% accuracy error with the PSD when more than 15 cm of clear optical fiber is irradiated. Conclusions: We developed a new PSD calibration formalism. After optimization of the calibration sequence of PSDs we showed improvements in both precision and accuracy of current PSD systems.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".