Sci—Thur PM: YIS — 01: Computational and Experimental Methods to Address the Limitations of Reconstructing Linac Photon Spectra from Transmission Measurements
Bibliographic record
Abstract
In the past two decades, the ill‐conditioned problem of unfolding linac photon spectra from transmission measurements has been extensively investigated. Previous studies suffer from various limitations that affect the accuracy and robustness of the unfolding. The goal of this study is to address the limitations of unfolding in general, and using parameterization of the spectrum in particular. To this end, the following new and refined methods are implemented. Different attenuator materials and different buildup caps are simultaneously used for better energy differentiation in most of the megavoltage energy range. The spectra are described using a new flexible and physics‐based functional form validated against more than 70 realistic spectra. The radiation detection system is modeled accurately using the EGSnrc usercodes BEAMnrc and cavity. Forward Compton scatter in the attenuators is modeled and corrected for. The proposed methods are validated experimentally on NRC research linac whose incident electron beam parameters are accurately known to 0.5% and its spectra have been independently measured using a NaI detector. A linear system was built to automatically drive attenuator lengths one at a time into the beam, which reduces acquisition time and beam instability uncertainties. Causes for experimental Type B uncertainties (leakage, polarity and scatter) are being investigated, particularly for small transmission signals. Results show that the proposed methods significantly improve the accuracy and robustness of unfolding in the presence of realistic experimental noise. Improvements by factors of 4 and 8 have been achieved in the accuracy of spectral unfolding and maximum energy estimation, respectively.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".