Charge loss correction in CZT pixel detectors at low and high fluxes: analysis of positive and negative pulses
Bibliographic record
Abstract
Charge losses are typical drawbacks in cadmium–zinc–telluride (CZT) pixel detectors. The effects of these phenomena are strongly related to the interaction point of the photons and are more severe for photon interactions at the inter-pixel gap and near the pixelated anode. In this work, we present some original techniques able to correct charge losses in pixelated CZT detectors at both low and high fluxes. The height, the shape and the arrival time of collected- and induced-charge pulses with both positive and negative polarities are analysed to recover charge losses after the application of charge sharing addition (CSA). Sub-millimetre CZT pixel detectors, fabricated by different manufacturers (Redlen Technologies, Canada and IMEM-CNR, Italy), are investigated with both uncollimated radiation sources and collimated synchrotron X rays (Diamond Light Source, U. K.), at energies below and above the K-shell absorption energy of the CZT material. The processing of the detector pulses is performed through a digital approach. A 16-channel digital readout electronics was recently developed at University of Palermo (Italy), able to perform on-line multi-parameter analysis (event arrival time, pulse shape, pulse height) and fine coincidence analysis (coincidence time windows < 20 ns). These activities are in the framework of an international collaboration on the development of energy-resolved photon counting (ERPC) systems for high-flux spectroscopic X-ray imaging (5-150 keV).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".