A systematic characterization of the low-energy photon response of plastic scintillation detectors
Bibliographic record
Abstract
To characterize the low energy behavior of scintillating materials used in plastic scintillation detectors (PSDs), 3 PSDs were developed using polystyrene-based scintillating materials emitting in different wavelengths. These detectors were exposed to National Institute of Standards and Technology (NIST)-matched low-energy beams ranging from 20 kVp to 250 kVp, and to (137)Cs and (60)Co beams. The dose in polystyrene was compared to the dose in air measured by NIST-calibrated ionization chambers at the same location. Analysis of every beam quality spectrum was used to extract the beam parameters and the effective mass energy-absorption coefficient. Monte Carlo simulations were also performed to calculate the energy absorbed in the scintillators' volume. The scintillators' expected response was then compared to the experimental measurements and an energy-dependent correction factor was identified to account for low-energy quenching in the scintillators. The empirical Birks model was then compared to these values to verify its validity for low-energy electrons. The clear optical fiber response was below 0.2% of the scintillator's light for x-ray beams, indicating that a negligible amount of fluorescence contamination was produced. However, for higher-energy beams ((137)Cs and (60)Co), the scintillators' response was corrected for the Cerenkov stem effect. The scintillators' response increased by a factor of approximately 4 from a 20 kVp to a (60)Co beam. The decrease in sensitivity from ionization quenching reached a local minimum of about [Formula: see text] between 40 keV and 60 keV x-ray beam mean energy, but dropped by 20% for very low-energy (13 keV) beams. The Birks model may be used to fit the experimental data, but it must take into account the energy dependence of the kB quenching parameter. A detailed comprehension of intrinsic scintillator response is essential for proper calibration of PSD dosimeters for radiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".