Increasing efficiency of BEAMnrc‐simulated Co‐60 beams using directional source biasing
Bibliographic record
Abstract
PURPOSE: This study describes the implementation of a directional source biasing (DSB) scheme for efficiently simulating Cobalt-60 treatment heads using the BEAMnrc Monte Carlo code. Previous simulation of Co-60 beams with BEAMnrc was impractical because of the time required to track photons not directed into the treatment field and to simulate secondary charged particles. METHODS: In DSB, efficiency is increased by splitting each photon emitted by the Co-60 source a user-defined number of times. Only those split primary photons directed into a user-defined splitting field (encompassing the treatment field) are sampled, yielding many low-weight photons directed into the field. Efficiency can be further increased by taking advantage of radial symmetry at the top of the treatment head to reduce the number of split primary photons tracked in this portion. There is also an option to generate contaminant electrons in DSB. RESULTS: The DSB scheme in BEAMnrc increases the photon fluence calculation efficiency in a 10 × 10 cm(2) Co-60 beam by a factor of 1800 with a concurrent increase in contaminant electron fluence calculation efficiency by a factor of 1200. Implementation of DSB in beampp, a C++ code for accelerator simulations based on EGSnrc and the C++ class library, egspp, increases photon fluence efficiency by a factor of 2800 and contaminant electron fluence efficiency by a factor of 1600. Optimum splitting numbers are in the range of 20,000-40,000. For dose calculations in a water phantom (0.5 × 0.5 × 0.5 cm(3) voxels) this translates into a factor of ∼400 increase in dose calculation efficiency (all doses > 0.5 × Dmax). An example calculation of the ratio of dose to water to dose to chamber (the basis of the beam quality correction factor) to within 0.2% in a realistic chamber using a full simulation of a Co-60 treatment head as a source indicates the practicality of Co-60 simulations with DSB. CONCLUSIONS: The efficiency improvement resulting from DSB makes Monte Carlo commissioning of Co-60 beams and calculation of beam quality correction factors feasible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".