Azimuthal particle redistribution for the reduction of latent phase-space variance in Monte Carlo simulations
Bibliographic record
Abstract
It is well known that the use of a phase space in Monte Carlo simulation introduces a baseline level of variance that cannot be suppressed through the use of standard particle recycling techniques. This variance (termed latent phase-space variance by Sempau et al) can be a significant limiting factor in achieving accurate, low-uncertainty dose scoring results, especially near the surface of a phantom. A BEAMnrc component module (MCTWIST) has been developed to reduce the presence of latent variance in phase-space-based Monte Carlo simulations by implementing azimuthal particle redistribution (APR). For each recycled use of a phase-space particle a random rotation about the beam's central axis is applied, effectively utilizing cylindrical symmetry of the particle fluence and therefore providing a more accurate representation of the source. The MCTWIST module is unique in that no physical component is actually added to the accelerator geometry. Beam modifications are made by directly transforming particle characteristics outside of BEAMnrc/EGSnrc particle transport. Using MCTWIST, we have demonstrated a reduction in latent phase-space variance by more than a factor of 20, for a 10 x 10 cm(2) field, when compared to standard phase-space particle recycling techniques. The reduction in latent variance has enabled the achievement of dramatically smoother in-water dose profiles. This paper outlines the use of MCTWIST in Monte Carlo simulation and quantifies for the first time the latent variance reduction resulting from exploiting cylindrical phase-space symmetry.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".