Sci-Thur PM: YIS - 02: A validated approach for clinical linacs to accurately determine the photon spectra and the incident electron energy
Bibliographic record
Abstract
In clinical photon beams, independent determination of the photon spectra and the incident electron energy is useful for beam (re)commissioning and for detector response modelling. In this study, an approach is developed for that purpose, and validated on a research linac whose photon spectra and electron beams are directly and independently known. In this approach, an optimized combination of transmission curves is measured using multiple attenuators and detectors to maximize energy differentiation. For validation, transmission measurements are made for 8 beams from 10-30 MV, with bremsstrahlung targets from Be to Pb. A protocol is established to account for many influence quantities including linac drifts (2%), polarity (6%), ion recombination (0.2%), leakage (0.3%), room scatter (0.8%), non-ideal attenuation (1.5%), attenuator mass thickness (4%), and photonuclear effect (5.6%). The experimental accuracy on the smallest signals is 0.4%. EGSnrc is upgraded to model photonuclear attenuation (without tracking secondary particles), and then used to model the full experiment. For direct transmission comparisons, the agreement is 2%. This allows for an estimate of 0.5% on the upper limit of photon cross section uncertainties, which is much better than the current estimate of 1-2%. The unfolded spectra agree with the benchmark ones within 4.5%. The incident electron energy is accurate within 5%, with 95% confidence. The overall improvement over the commonly used methods is a factor of 3. This transmission study is the first to independently determine the incident electron energy, and to recognize the significant role of the photonuclear effect at higher energies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".