Corrections for Fluorescence and Charge-Sharing Effects to Bremsstrahlung Spectra for a Hyperspectral Pixelated CZT X-Ray Detector
Bibliographic record
Abstract
A Monte Carlo model was developed to simulate the response of a pixelated hyperspectral CdZnTe (CZT) X-ray detector. The first part of the simulation was carried out using Geant4 to obtain a list of energy depositions inside the CZT crystal. The second part of the simulation used charge transport equations to calculate the size of the electron charge cloud, as it drifts under the influence of an electric field to be read out. Experimentally acquired data from an Am-241 source with the high energy X-ray imaging technology (HEXITEC) detector were compared to simulated data, and good agreement was found. The model was used to investigate the energy dependence of fluorescence and charge-sharing effects. The probability of producing an escaped fluorescence photon was quantified as a function of primary photon energy. As expected, at primary photon energies just above the K-edge of Cd, there was a greater chance of producing an escaped fluorescence photon, and this probability decreased as the primary photon energy increased. The probability of an event being shared across multiple pixels as a function of primary photon energy was quantified. It was found that as the primary photon energy was increased, there was a greater chance of producing an event shared across multiple pixels. The detector response to a Bremsstrahlung spectrum was simulated. Using previous results, fluorescence and charge-sharing effects were corrected for, giving a corrected spectrum in good agreement with the input spectrum.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".