Efficient photon beam dose calculations using DOSXYZnrc with BEAMnrc
Bibliographic record
Abstract
This study examines the efficiencies of doses calculated using DOSXYZnrc for 18 MV (10 X 10 cm2 field size) and 6 MV (10 X 10 cm2 and 20 X 20 cm2 field sizes) photon beams simulated using BEAMnrc. Both phase-space sources and full BEAMnrc simulation sources are used in the DOSXYZnrc calculations. BEAMnrc simulation sources consist of a BEAMnrc accelerator simulation compiled as a shared library and run by the user code (DOSXYZnrc in this case) to generate source particles. Their main advantage is in eliminating the need to store intermediate phase-space files. In addition, the efficiency improvements due to photon splitting and particle recycling in the DOSXYZnrc simulation are examined. It is found that photon splitting increases dose calculation efficiency by a factor of up to 6.5, depending on beam energy, field size, voxel size, and the type of secondary collimation used in the BEAMnrc simulation (multileaf collimator vs photon jaws). The optimum efficiency with photon splitting is approximately 55% higher than that with particle recycling, indicating that, while most of the gain is due to time saved by reusing source particle data, there is significant gain due to the uniform distribution of interaction sites and faster DOSXYZnrc simulation time when photon splitting is employed. Use of optimized directional bremsstrahlung splitting in the BEAMnrc simulation sources increases the efficiency of photon beam simulations sufficiently that the peak efficiencies (i.e., with optimum setting of the photon splitting number) of DOSXYZnrc simulations using these sources are only 3-13% lower than those with phase-space file sources. This points towards eliminating the need for storing intermediate phase-space files.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".