The influence of nuclear interactions on ionization chamber perturbation factors in proton beams: FLUKA simulations supported by a Fano test
Bibliographic record
Abstract
Purpose In all recent protocols for the reference dosimetry of clinical proton beams, ionization chamber perturbation factors are assumed to be unity. In this work, such factors were computed using the FLUKA Monte Carlo code for three ionization chamber types, with particular attention to the influence of nuclear interactions. Methods The accuracy of the transport algorithms implemented in FLUKA was first evaluated by performing a Fano cavity test. Ionization chamber perturbation factors were computed for the PTW‐34001 Roos ® and the PTW‐34070 and PTW‐34073 Bragg peak ® chambers for proton beams of 60–250 MeV using the same transport parameters that were needed to pass the Fano test. Results FLUKA was found to pass the Fano test within 0.15%. Ionization chamber simulation results show that the presence of the air cavity and the wall results in dose perturbations of the order of 0.6% and 0.8%, respectively. The perturbation factors are shown to be energy dependent and nuclear interactions must be taken into account for accurate calculation of the ionization chamber's response. Conclusion Ionization chamber perturbations can amount to 1% in high‐energy proton beams and therefore need to be considered in dosimetry procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".