TU-AB-BRC-07: Efficiency of An IAEA Phase-Space Source for a Low Energy X-Ray Tube Using Egs++
Bibliographic record
Abstract
Purpose: To extend the capability of the EGSnrc C++ class library (egs++) to write and read IAEA phase-space files as a particle source, and to assess the relative efficiency gain in dose calculation using an IAEA phase-space source for modelling a miniature low energy x-ray source. Methods: We created a new ausgab object to score particles exiting a user-defined geometry and write them to an IAEA phase-space file. A new particle source was created to read from IAEA phase-space data. With these tools, a phase-space file was generated for particles exiting a miniature 50 kVp x-ray tube (The INTRABEAM System, Carl Zeiss). The phase-space source was validated by comparing calculated PDDs with a full electron source simulation of the INTRABEAM. The dose calculation efficiency gain of the phase-space source was determined relative to the full simulation. The efficiency gain as a function of i) depth in water, and ii) job parallelization was investigated. Results: The phase-space and electron source PDDs were found to agree to 0.5% RMS, comparable to statistical uncertainties. The use of a phase-space source for the INTRABEAM led to a relative efficiency gain of greater than 20 over the full electron source simulation, with an increase of up to a factor of 196. The efficiency gain was found to decrease with depth in water, due to the influence of scattering. Job parallelization (across 2 to 256 cores) was not found to have any detrimental effect on efficiency gain. Conclusion: A set of tools has been developed for writing and reading IAEA phase-space files, which can be used with any egs++ user code. For simulation of a low energy x-ray tube, the use of a phase-space source was found to increase the relative dose calculation efficiency by factor of up to 196. The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant No. 432290).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".