Corrections to the Hecht collection efficiency in photoconductive detectors under large signals: non-uniform electric field due to drifting and trapped unipolar carriers
Bibliographic record
Abstract
Abstract The Hecht collection efficiency η 0 , and its modified expressions for exponential absorption, have been widely used in time-of-flight type transient photoconductivity experiments as well as in the assessment of the sensitivity of integrating-type radiation detectors. However, the equations apply under small signals in which the internal field remains uniform (unperturbed). We have used Monte Carlo simulation and the numerical solution of the continuity, trapping rate and Poisson equations to calculate the collection efficiency η r (CE) for various levels of charge injection and deep trapping. The carriers are injected instantaneously very near the radiation receiving electrode and then drift under space charge perturbed conditions. The CE deviation from the ideal Hecht value has been quantified in terms of the injection ratio r and the normalized trapping time τ with respect to the transit time under small signals. The results can be represented by a scaled, compressed exponential with coefficients that depend on τ . A plot is provided for these coefficients. The CE drops significantly below the Hecht value as r increases and the deviation is more pronounced for smaller τ values. The errors in extracting τ from the application of the Hecht equation has been also calculated and mapped as a function of different r and τ values.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".