Sci-Fri PM: Dosimetry-02: A Nested Neutron Spectrometer to Measure Neutron Spectra in Radiotherapy
Bibliographic record
Abstract
During high-energy radiotherapy treatments, neutrons are produced in the head of the linac through photonuclear interactions. This has been a concern for many years as photoneutrons contribute to the accepted, yet unwanted, out-of-field doses that pose an iatrogenic risk to patients and an occupational risk to personnel. Presently, in-room neutron measurements are difficult and time-consuming and have traditionally been carried out using Bonner spheres with activation foils and TLDs. In this work, a new detector, the Nested Neutron Spectrometer (NNS) is tested for use in radiotherapy bunkers. The NNS is designed for easy handling and is more practical than the traditional Bonner spheres. The NNS, operated in current mode, was used to measure the dose equivalent, average energy and energy spectrum at several positions in a radiotherapy bunker. The average energy and spectra were compared to Monte Carlo simulations while the dose equivalent was compared to bubble detector measurements. The average energies, as measured by the NNS and Monte Carlo simulations, differed by approximately 30% across the bunker. Measurements of the dose equivalent using the NNS and the bubble detectors agreed within 50% in the maze and less than 10% close to the linac head. Apart from some discrepancies at thermal energies, we also found reasonable agreement between NNS-measured and Monte Carlo-simulated spectra at a number of locations within our radiotherapy bunker. Our results demonstrate that the NNS is a suitable detector to be used in high dose-rate radiotherapy environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".