SU-FF-T-349: PMC, a New Fast Monte Carlo Code for Radiation Therapy
Bibliographic record
Abstract
Introduction: A fast and accurate MC code, have been developed. PMC takes the advantage of large available memory in current computer hardware for extensive generation of pre-calculated data. Methods: The tracks of 5000 primary electrons are generated in the middle of a large homogenous phantom for various materials (water, air, bone, lung, and tissue) and energies (0.2,0.4, …1,2,…, 18 MeV) using EGSnrc code. The maximum electron steps is controlled by setting ximax=0.02. The secondary electrons are not transported but its position; energy, charge, direction are saved. In PMC using the energy and medium of the incident electron, one track is selected from the related set. The selected track is then transported and rotated to the position and direction of incident electron and the transport starts. If the electron reaches a new material according to its energy a new track is picked up from related material. If a secondary electron and its energy is above the PMC cut offs (ECUT=100KeV) its position, charge, energy and direction are saved in the stack. Once the track of the primary electron is finished each secondary is transported in the same manner as primary electron. For various energies a track from closest energy set is picked up and linear scaling is done for deposited energy and track length. Results and discussion: The performance of the code is tested in various homogenous and heterogeneous phantoms and the results had very good agreement (up to 1.6%) with EGS and it runs 40 times faster than EGS. Conclusion: Not a single physical calculation is done in PMC code even in the presence of heterogeneities. The pre-calculated data is generated for each particular material and this improves the performance of code both in terms of accuracy and speed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".