SU‐F‐J‐152: Accuracy of Charge Particle Transport in Magnetic Fields Using EGSnrc
Bibliographic record
Abstract
Purpose: Determine accuracy of the current implementation of electron transport under magnetic fields in EGSnrc by means of single scattering (SS) and Fano convergence tests, and establish quantitatively the electron step size restriction required to achieve a desired level of accuracy for ionization chamber dosimetry. Methods: Condensed history (CH) dose calculations are compared to SS results for a PTW30013 ionization chamber irradiated in air by a 60Co photon beam. CH dose results for this chamber irradiated in a water phantom by a source of mono‐energetic electrons are compared to the prediction of Fano's theorem for step size restrictions EM ESTEPE from 0.01 to 0.1 and strengths of 0.5 T, 1.0 T, and 1.5 T. Results: CH calculations in air for 60Co photons using an EM ESTEPE of 0.25 overestimate SS values by 6% for a 1.5 T field and by 1.5% for a 0.5 T field. Agreement improves with decreasing EM ESTEPE reducing this difference at 0.02 to 0.13% and 0.04% for 1.5 T and 0.5 T respectively. CH results converge with decreasing EM ESTEPE reaching an agreement of 0.2% at a value of EM ESTEPE of 0.01 for 100 keV electrons. SS results at 100 keV for 1.5 T show the same EM ESTEPE dependency as the CH results. Conclusion: Accurate transport of charged particles in magnetic fields is only possible if the step size is significantly restricted. An EM ESTEPE value of 0.02 is required to reproduce SS results at the 0.1% level for a calculation in air. The EM ESTEPE dependency of the SS results suggests SS is bypassed when simulating the transport of charged particles in magnetic fields. Fano test results for in water calculation suggest that only a 0.2% accuracy can be achieved with the current implementation.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".