SU-E-T-72: Influence of Chamber Wall Material On Ionization Chamber Absorbed Dose Energy Response: A Numerical and Experimental Study
Bibliographic record
Abstract
Purpose: To study the energy response of ionization chambers with different wall materials. Methods: The dose inside the cavity of an accurately modeled Exradin A12 ionization chamber was scored with Monte Carlo user code egs++/egs_chamber. The C552 plastic chamber wall material was changed in the simulations with materials of higher atomic number Z (aluminum, copper, molybdenum and tungsten) and simulations were carried out with five beam energies ranging from 120 kVp to 18 MV. The dose scored inside the cavity for each wall material was normalized to that from a C552 plastic wall. The mean secondary electron energy for each beam Ee- was also calculated at the level of chamber cavity using MC user code FLURZnrc. The simulations were experimentally verified by replacing the Exradin A12 chamber C552 wall with Al or Cu walls of identical dimensions. AAPM TG51 and TG61 setups were followed for high and low energy beams, respectively. Large attenuation of kilovoltage photons by high Z wall materials was accounted for by correcting the readings with a CAVRZnrc-calculated chamber wall attenuation and scatter correction Awall. Results: The relative readings obtained showed that with the use of higher Z wall materials, the chamber signal increased by up to a factor of 2.96 for MV photons, and 54.71 at 120 kVp. Higher Z walls Result in larger contribution of photoelectrons, and as such changing the wall material significantly affects the absorbed dose energy-dependence of the chambers. Experimental results agree with simulations to within 9.8 %. The discrepancy is largest at kV beams and can be mitigated if the impurities found in each wall material were considered in the MC simulations. Conclusion: The change in ionization chamber absorbed-dose energy dependence is studied (numerically and experimentally), by replacing the original wall chamber with walls of different Z material, while keeping the wall dimensions identical.
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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) | 0.001 | 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".