Dose discrepancies between Monte Carlo calculations and measurements in the buildup region for a high‐energy photon beam
Bibliographic record
Abstract
This study investigates a possible cause of reported significant dose discrepancies between Monte Carlo calculations and measurements in the buildup region for high-energy photon beams in large fields. A proposed hypothesis was that the discrepancy was caused by a source of electrons in the accelerator head that was not fully accounted for in the treatment head simulation. In this investigation, a lead foil is added just below the accelerator head in order to study this hypothesis. The lead foil effectively removes charged particles generated inside the accelerator head. The charged particles generated by the lead foil can be accounted for fully because the simple geometry can be simulated accurately. An 18 MV photon beam from a Varian Clinac-2100EX is measured using a WELLHOFER WP700 beam scanner with an IC-10 ionization chamber (cavity radius=3 mm). The BEAM Monte Carlo code is used in the incident beam simulations. Both EGS4/DOSXYZ and EGSnrc/DOSRZnrc are used in the dose calculations in a water phantom. The Monte Carlo calculated depth-dose curve is scaled so that it has the same values at 10 cm depth as the measured curve. It is found that the discrepancies between Monte Carlo calculations and measurements remain significant in the buildup region even after applying necessary corrections to the measured data. The discrepancies have only been modestly decreased with the lead foil in place compared to the 40 x 40 cm2 open field. At a depth of 1 cm, discrepancies of about 5% are still observed in the buildup region for the field with the lead foil. Therefore a new explanation for the unresolved discrepancy remains to be found.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".