Poster ‐ 18: New features in EGSnrc for photon cross sections
Bibliographic record
Abstract
Purpose: To implement two new features in the EGSnrc Monte Carlo system. The first is an option to account for photonuclear attenuation, which can contribute a few percent to the total cross section at the higher end of the energy range of interest to medical physics. The second is an option to use exact NIST XCOM photon cross sections. Methods: For the first feature, the photonuclear total cross sections are generated from the IAEA evaluated data. In the current, first‐order implementation, after a photonuclear event, there is no energy deposition or secondary particle generation. The implementation is validated against deterministic calculations and experimental measurements of transmission signals. For the second feature, before this work, if the user explicitly requested XCOM photon cross sections, EGSnrc still used its own internal incoherent scattering cross sections. These differ by up to 2% from XCOM data between 30 keV and 40 MeV. After this work, exact XCOM incoherent scattering cross sections are an available option. Minor interpolation artifacts in pair and triplet XCOM cross sections are also addressed. The default for photon cross section in EGSnrc is XCOM except for the new incoherent scattering cross sections, which have to be explicitly requested. The photonuclear, incoherent, pair and triplet data from this work are available for elements and compounds for photon energies from 1 keV to 100 GeV. Results: Both features are implemented and validated in EGSnrc.Conclusions: The two features are part of the standard EGSnrc distribution as of version 4.2.3.2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.025 |
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".