TH‐C‐303A‐10: Simulation of An In‐Line 6MV Linear Accelerator From Electron Gun to Target
Bibliographic record
Abstract
Purpose: To report on the development of a comprehensive linear accelerator (linac) model that simulates the electron dynamics from the electron gun to the target. This model was developed to determine the maximum tolerable magnetic field strength that a linac can operate in, so that passive magnetic shielding can be optimized for use in a large scale linac‐MR system which is currently being developed. The design of magnetic shielding is essential for the linac and MR systems to be coupled together making true real‐time adaptive radiotherapy possible. Method and Materials: The finite element method (FEM) was used to solve the radiofrequency (RF) fields within an in‐house designed 6MV in‐line linac waveguide. A Pierce‐type diode electron gun was designed and the emitted electron trajectories were solved using the particle tracking code EGN2w. The injected electron trajectories and solved RF fields then served as inputs into the particle tracking code PARMELA. Benchmarking of the simulation was performed by taking the electron phase space generated at the target, running it through the BEAMnrc and DOSEXYZnrc Monte Carlo algorithms and comparing the simulated dose distributions to measured values at various field sizes and depths. Results: Discrepancies of less than 0.04% were found when comparing the 3D FEM RF fields to the benchmarked program Poisson Superfish while the simulated dose results were found to be within 1% of measurement for the dose profile horns of various field sizes at various depths. Conclusion: A full simulation of an in‐line 6MV linac waveguide from electron gun to target has been benchmarked, showing agreements of the simulated dose distributions to within 1% of measurement. By adding the known magnetic fringe fields from the bi‐planar magnet, the exact extent of electron trajectory deflections can be determined and thus the optimal magnetic shielding can be designed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".