Development of a multi-layer silicon beta-ray spectrometer for beta spectrometry and dosimetry at CANDU power plants
Bibliographic record
Abstract
Radiation-induced cataractogenesis is a growing concern as a stochastic effect for workers exposed to a mixed beta-gamma radiation field observed at Canada Deuterium Uranium (CANDU) power plants. Accurate beta dosimetry to the lens of the eyes in a mixed field remains a challenge due to limitations in existing beta spectrometers. In this dissertation, a compact multi-layer Silicon Beta-ray Spectrometer (SBS) has been developed for beta spectrometry and dosimetry. Its design is based on the principle that the coincidence operations between silicon detectors make most gamma detection events be rejected, while beta detection events are saved in the beta energy region of 0.7 to 3 MeV. A prototype spectrometer consists of a collimator, an entrance window, a stack of silicon detectors, an interface board, and a quad-input pulse processing system. Monte Carlo simulations were carried out to optimize the configuration of the detector stack and compute the spectrometer response matrices to beta and gamma radiation. To characterize the gamma rejection and beta spectrometric performance, comprehensive measurements were carried out for various mixed beta-gamma fields with different beta count rates and beta-gamma count ratios that were created by varying the positions of a 90Sr/90Y beta source and a137Cs gamma source. The coincidence spectra showed excellent gamma rejection performance in most energy regions above 250 keV, while notable gamma perturbation events were identified in the low energy region for the coincidence spectrum betweenthe first two detectors and the anti-coincidence spectrum of the front detector. Additional experiments with low-level waste samples highlighted the SBS’s enhanced detection capability for low activity sources thanks to background suppression. A custom microcontroller-based digital signal processing system was developed as a compact, cost-effective alternative to commercial systems. It supports onboard coincidence and achieved comparable performance in certain metrics, though issues in pulse processing and histogram updating affected low-energy event detection in specific channels. Finally, a fully Bayesian unfolding pipeline was built to derive the fluence spectra from measured coincidence spectra. Simulations validated its capability to unfold beta and gamma fluence spectra simultaneously, with promising results for beta-to-gamma ratios down to 0.1. However, performance degraded at lower beta-to-gamma ratio due to gamma perturbation, and application to lab measurements faced challenges from reduced coincidence efficiencies and calibration limitations. This work successfully demonstrated the SBS prototype as a key milestone. Extensive discussions for future improvements were given for the full SBS instrument.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".