An analytic model for the response of a CZT detector in diagnostic energy dispersive x‐ray spectroscopy
Bibliographic record
Abstract
A CdZnTe detector (CZTD) can be very useful for measuring diagnostic x-ray spectra. The semiconductor detector does, however, exhibit poor hole transport properties and fluorescence generation upon atomic de-excitations. This article describes an analytic model to characterize these two phenomena that occur when a CZTD is exposed to diagnostic x rays. The analytical detector response functions compare well with those obtained via Monte Carlo calculations. The response functions were applied to 50, 80, and 110 kV x-ray spectra. Two 50 kV spectra were measured; one with no filtration and the other with 1.35 mm Al filtration. The unfiltered spectrum was numerically filtered with 1.35 mm of Al in order to see whether the recovered spectrum resembled the filtered spectrum actually measured. A deviation curve was obtained by subtracting one curve from the other on an energy bin by bin basis. The deviation pattern fluctuated around the zero line when corrections were applied to both spectra. Significant deviations from zero towards the lower energies were observed when the uncorrected spectra were used. Beside visual observations, the exposure obtained using the numerically attenuated unfiltered beam was compared to the exposure calculated with the actual filtered beam. The percent differences were 0.8% when corrections were applied and 25% for no corrections. The model can be used to correct diagnostic x-ray spectra measured with a CdZnTe detector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".