Sci‐Fri PM: Planning‐01: Measured electron and x‐ray angular distribution data for benchmarking Monte Carlo codes
Bibliographic record
Abstract
Monte Carlo (MC) studies of the output of medical linear accelerators have demonstrated that in-air profiles are useful in the beam commissioning process. A recent investigation of x-ray profiles (Tonkopi et al, Med. Phys 32 (9), 2005) showed very good agreement between measurement and EGSnrc calculations but to achieve this level of agreement the beam linac spot size, energy and angular divergence had to be treated as variables. In this project we carried out measurements and MC calculations for an electron accelerator for which the initial beam parameters are well known. Two sets of investigations were carried out. In the first we measured electron scatter distributions for a range of scattering foils and electron energies of 13 and 20 MeV. The profiles were parameterised and compared to EGSnrc Monte Carlo calculations. It was found that generally the EGSnrc calculations gave agreement with the measurements within 1.5 %. In the second investigation, which is on-going, in-air profiles were obtained for photon beams produced using different targets (from beryllium to lead). Measured angular distributions were obtained using ion chambers with different build-up caps (low and high-Z) and the sensitivity of the data to small changes in geometry (e.g., moving the x-ray target) was investigated. The photon energy fluence was calculated using EGSnrc and preliminary indications are that the measured and calculated distributions agree to better than 5 %. Work supported in part by NIH grant R01 CA104777-01A2.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".