Effects of non-uniform electric field on the charge collection efficiency in radiation detectors: Deviation from Hecht formula
Bibliographic record
Abstract
The Charge Collection Efficiency (CCE) of a radiation detector under a non-uniform electric field has been examined by developing a semi-analytical and a numerical model. We consider the Carrier Packet Drift Analysis for the semi-analytical model and a numerical solution of the continuity and Poisson‘s equations for the numerical model. The space charge due to ionized dopants and the trapped charges of photogenerated carriers in the bulk photoconductor layer of the detector are considered. We analyze the electric field distributions and CCE of the detector under various charge carrier transport parameters and detector operating conditions. The CCE under the non-uniform electric field deviates significantly from the uniform field case as determined by the Hecht Collection Efficiency (HCE) formula. In most cases, the CCE under the non-uniform electric field deteriorates as compared to HCE. However, the CCE under the non-uniform electric field can also be improved for certain values of normalized carrier lifetimes (ratio of carrier lifetime to its transit time) provided that the radiation (photons or particles) absorption occurs mostly near the radiation-receiving electrode. The theoretical model has been applied to the published experimental results in perovskite x-ray detectors for general radiographic applications and found good agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".