Sci‐Fri AM: YIS‐03: Simulated annealing optimization of the pre‐target electron beam in Monte Carlo virtual linac models
Bibliographic record
Abstract
The purpose of this study is to develop a method for determining the initial parameters of the pre‐target electron beam within a Monte Carlo (MC) accelerator model able to produce accurate 18 MV 40×40 cm2 photon field profiles. We have developed a novel method by which the electron beam intensity distribution can be reverse engineered to reproduce measured dose distributions. The method begins from a cylindrically symmetric pre‐target electron beam (radius 0.5 cm) of uniform intensity. This beam is subdivided into annular regions of fluence for which each region is individually transported through the accelerator head and into a water phantom. A simulated annealing search is then performed to determine the optimal combination of weights of the annular fluences that provide a best match between measured dose distributions and the weighted sum of annular dose distributions. Remarkably, the intensity distribution converges to a solution that is predominantly Gaussian, with a FWHM=1.1mm. In addition, the solution contains an important secondary “extra focal halo” on the order of 10% of the maximum Gaussian intensity. Agreement of the 40×40 cm2 photon field profiles with measurement was within 0.5%. The method greatly reduces the effort required to commission a MC accelerator model for clinical use and has achieved better agreement with measurement than other methods described in the literature. Our derived value of the electron beam FWHM agrees with that measured by Jaffray et al, 1993, and the “extra focal halo” is in qualitative agreement with their measurements of extra focal radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".