Monte Carlo modeling of the Varian TrueBeam linear accelerator, with chamber effects included in determination of the source parameters
Bibliographic record
Abstract
Varian has opted not to provide the physical details of their TrueBeam linear accelerator, which has historically been provided to researchers under non-disclosure agreements to construct accurate Monte Carlo beam models. One compromise has been to release Monte Carlo calculated phase space data above the linac jaws, with source parameters tuned to TrueBeam representative beam data. In addition, Varian has also developed a cloud-based VirtuaLinac web interface that allows for the generation of phase space data with customizable source parameters on a pay-as-you-go basis, without knowing the geometric details within the Geant4 model. The main disadvantages of Varian phase space sources are the efficiency and cost when compared to a full BEAMnrc/EGSnrc Monte Carlo beam model. Phase space sources can have massive storage requirements, their network and hard drive use can drastically slow down simulations, and the cost is not insignificant for detailed tuning of VirtuaLinac source parameters to in-house measurements.FalseBEAM, an independent TrueBeam model for 6 MV flattening filter free (FFF) photon beams, has been constructed by modifying a previously commissioned 6 MV model of the Varian Clinac 21EX in BEAMnrc. Geometric dimensions and materials were matched in a trial and error approach to the photon/electron fluence and spectra of Varian phase space files. Once the in-house model phase space matched that of the Varian phase space with identical source parameters, the source parameters were tuned to match in-house water tank measurements obtained with the PTW microDiamond detector. In addition, a BEAMnrc implementation of a published PENELOPE TrueBeam model, FakeBeam, was investigated. Detector models were included in all dose to water simulations to include the effects of volume averaging and the non-water equivalence of the detector materials, which allowed for more accurate selection of beam model source parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".