Spatiotemporal Monte Carlo transport methods in x-ray semiconductor detectors: Application to pulse-height spectroscopy in a-Se
Bibliographic record
Abstract
PURPOSE: The authors describe a detailed Monte Carlo (MC) method for the coupled transport of ionizing particles and charge carriers in amorphous selenium (a-Se) semiconductor x-ray detectors, and model the effect of statistical variations on the detected signal. METHODS: A detailed transport code was developed for modeling the signal formation process in semiconductor x-ray detectors. The charge transport routines include three-dimensional spatial and temporal models of electron-hole pair transport taking into account recombination and trapping. Many electron-hole pairs are created simultaneously in bursts from energy deposition events. Carrier transport processes include drift due to external field and Coulombic interactions, and diffusion due to Brownian motion. RESULTS: Pulse-height spectra (PHS) have been simulated with different transport conditions for a range of monoenergetic incident x-ray energies and mammography radiation beam qualities. Two methods for calculating Swank factors from simulated PHS are shown, one using the entire PHS distribution, and the other using the photopeak. The latter ignores contributions from Compton scattering and K-fluorescence. Comparisons differ by approximately 2% between experimental measurements and simulations. CONCLUSIONS: The a-Se x-ray detector PHS responses simulated in this work include three-dimensional spatial and temporal transport of electron-hole pairs. These PHS were used to calculate the Swank factor and compare it with experimental measurements. The Swank factor was shown to be a function of x-ray energy and applied electric field. Trapping and recombination models are all shown to affect the Swank factor.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".