On calculating kerma, collision kerma and radiative yields
Bibliographic record
Abstract
Purpose To study the relationships between dose ( D ), kerma ( K ), and collision kerma ( ) in photon beams and to investigate total radiative yields for electrons and positrons as a function of energy. To do this accurately required calculating collision kerma directly as a function of position in a phantom and making changes to the EGSnrc package (including DOSRZnrc and g applications). Methods Changes were made to the EGSnrc system to allow the user to distinguish events according to their initiating process, most importantly relaxation particles initiated by electron impact ionization as opposed to initiated by photons, especially those events depositing energy below energy cutoffs after relaxation events. Appropriate changes were made to the applications DOSRZnrc and g and a new application, DOSRZnrcKcol , was written. Results The modified codes are much more robust against changes in simulation parameters such as ECUT and AE and whether or not electron impact ionization is included in the simulation. The radiative yields for electrons generally differ from values in ICRU Report 37 1 (1984) which only account for radiative losses due to bremsstrahlung. The Monte Carlo calculated g (brems) values are generally greater than the ICRU 37 values due to energy‐loss straggling. Plots of D , K and vs depth in megavoltage photon beams show that some “conventional wisdom” does not hold in general ( e.g ., D is not always greater than K past ) and in general at 10 cm depth it is found that D ≈ K and . The beam radius at which reaches its saturation value depends strongly on the threshold used to define reaching saturation and is generally greater than the radius for lateral charged particle equilibrium used in the TRS‐483 Code of Practice for small beam dosimetry 2 (Palmans et al, Med Phys . 2018; 45 :e1123–e1145). Conclusions The changes to EGSnrc make kerma calculations more accurate but previous calculations with electron impact ionization turned off gave close to correct results. The application DOSRZnrcKcol makes calculating collision kerma more efficient and avoids various approximations used in the past although those approximations are shown to be justified. Including energy‐loss straggling when calculating bremsstrahlung radiation yield increases the value. Fluorescence losses and annihilation in flight further increase the radiation yield of electrons and positrons. Results demonstrate the effects of EGSnrc using electron bremsstrahlung production cross sections for positrons and failing to model positron impact ionization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".