TU‐C‐108‐08: Characterization of a Fiber‐Taper CCD Photo‐Counting System for Plastic Scintillation Dosimetry and Comparison to the Traditional Lens System
Bibliographic record
Abstract
Purpose: To build a lens‐less CCD photo‐counting system for plastic scintillation dosimeters (PSDs), characterize its signal‐to‐noise ratio (SNR), its dose and dose‐rate sensitivity, and compare it to traditional CCD+lens optical systems. Methods: The detector used in this study is made of a 1 mm diameter by 2 mm long BCF60 scintillating‐fiber coupled to a 2.6 meters clear plastic fiber. This PSD is coupled to two different photon‐counting systems. The first is composed of a fiber‐taper system (FTS) of a 2048 × 2048 pixels attached to an uncooled Alta (Apogee) 4020 polychromatic CCD‐camera and a second one consisting of a 1600 × 1200 pixels, Alta (Apogee) 2020 polychromatic CCD‐camera (cooled to ‐ 18°C) using a 50 mm lens of F#=1. The same identical dose measurements were made under the same irradiation conditions. For both systems, the chromatic Cerenkov removal method is used to account for the stem effects. Results: The FTS increases the light collection by a factor of 4. This gain is possible because we are no longer restricted by the geometric limitation or the optical aberration of a lens system. Despite a difference of 45°C in operating temperature, we observed a SNR 1.8 times higher. This produces a good stability of measure vs. integration time with at most 1% difference on low‐dose measurements. This also reduces by at least a factor of 2 the uncertainties, allowing us to realize low‐dose measurements of 1.0 and 0.5 cGy with accuracy of 3.4% and 5.6% vs. 5.8% and 15.9% for the lens‐based system. Conclusion: This study demonstrates the superiority of the simpler FTS over a lens‐based system. It opens the possibility of using PSDs of smaller radii (125 micron) to obtain greater spatial resolution. Reducing the size of PSDs could further simplify their use for in‐vivo monitoring and small field dosimetry. Development and funding supported by Standard 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".