Efficiency improvements of x‐ray simulations in EGSnrc user‐codes using bremsstrahlung cross‐section enhancement (BCSE)
Bibliographic record
Abstract
This paper presents the implementation of the bremsstrahlung cross-section enhancement (BCSE) variance-reduction technique into the EGSnrc/BEAMnrc system. BCSE makes the simulation of x-ray production from bremsstrahlung targets more efficient; it does so by artificially making the rare event of bremsstrahlung emission more abundant, which increases the number of statistically-independent photons that contribute to reducing the variance of the quantity of interest without increasing the CPU time appreciably. BCSE does not perturb the charged-particle transport in EGSnrc and it is made compatible with all other variance-reduction techniques already used in EGSnrc and BEAMnrc, including range rejection, uniform bremsstrahlung splitting, and directional bremsstrahlung splitting. When optimally combining BCSE with splitting to simulate typical situations of interest in medical physics research and in clinical practice, efficiencies can be up to five orders of magnitude larger than those obtained with analog simulations, and up to a full order of magnitude larger than those obtained with optimized splitting alone (which is the state-of-the-art of the EGSnrc/BEAMnrc system before this study was carried out). This study recommends that BCSE be combined with the existing splitting techniques for all EGSnrc/BEAMnrc simulations that involve bremsstrahlung targets, both in the kilovoltage and megavoltage range. Optimum crosssection enhancement factors for typical situations in diagnostic x-ray imaging and in radiotherapy are recommended, along with an easy algorithm for simulation optimization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".