Effect of transverse magnetic fields on a simulated in-line 6 MV linac
Bibliographic record
Abstract
The effects of a transverse magnetic field on an in-line side-coupled 6 MV linear accelerator are given. The results are directly applicable to a linac-MR system used for real-time image guided adaptive radiotherapy. Our previously designed end-to-end linac simulation incorporated the results from the axisymmetric 2D electron gun program EGN2w. However, since the magnetic fields being investigated are non-axisymmetric in nature for the work presented here, the electron gun simulation was performed using OPERA-3d/SCALA. The simulation results from OPERA-3d/SCALA showed excellent agreement with previous results. Upon the addition of external magnetic fields to our fully 3D linac simulation, it was found that a transverse magnetic field of 6 G resulted in a 45 +/- 1% beam loss, and by 14 G, no electrons were incident on the target. Transverse magnetic fields on the linac simulation produced a highly asymmetric focal spot at the target, which translated into a 13% profile asymmetry at 6 G. Upon translating the focal spot with respect to the target coordinates, profile symmetry was regained at the expense of a lateral shift in the dose profiles. It was found that all points in the penumbra failed a 1%/1 mm acceptance criterion for fields between 4 and 6 G. However, it was also found that the lateral profile shifts were corrected by adjusting the jaw positions asymmetrically.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".