Technical Note: Effect of explicit <i>M</i> and <i>N</i>‐shell atomic transitions on a low‐energy x‐ray source
Bibliographic record
Abstract
PURPOSE: In EGSnrc, atomic transitions to and from the M and N-shells are treated in an average way by default. This approach is justified in which the energy difference between explicit and average M and N-shell binding energies is less than 1 keV, and for most applications can be considered negligible. However, for simulations of low energy x-ray sources on thin, high-Z targets, characteristic x-rays can make up a significant portion of the source spectra. As of release V4-2.4.0, EGSnrc has included an option to enable a more complete algorithm of all atomic transitions available in the EADL compilation. In this paper, the effect of M and N-shell averaging on the calculation of half-value layer (HVL) and relative depth dose (RDD) curve of a 50 kVp intraoperative x-ray tube with a thin gold target was investigated. METHODS: A 50 kVp miniature x-ray source with a gold target (The INTRABEAM System, Carl Zeiss, Germany) was modeled with the EGSnrc user code cavity, both with and without M and N-shell averaging. From photon fluence spectra simulations, the source HVLs were determined analytically. The same source model was then used with egs_chamber to calculate RDD curves in water. RESULTS: A 4% increase of HVL was reported when accounting for explicit M and N-shell transitions, and up to a 9% decrease in local relative dose for normalization at 3 mm depth in water. CONCLUSIONS: The EGSnrc default of using averaged M and N-shell binding energies has an observable effect on the HVL and RDD of a low energy x-ray source with high-Z target. For accurate modeling of this class of devices, explicit atomic transitions should be included.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".