The effects of large signals on charge collection in radiation detectors: Application to amorphous selenium detectors
Bibliographic record
Abstract
Analytical and numerical models for studying the effects of large signals on charge collection efficiency in radiation detectors are described by considering bimolecular recombination between drifting charge carriers, carrier trapping, and space charge effects. First, an analytical solution is obtained by assuming that the field remains relatively uniform. Then the continuity equations for both holes and electrons, and Poisson’s equation across the photoconductor for a short pulse irradiation are simultaneously solved by the finite difference method, without any assumptions. There is a very good agreement between the approximate analytical and numerical solutions for the charge collection efficiency. The numerical results are also compared with Monte Carlo simulations of carrier transport. The charge collection efficiency model is applied to amorphous selenium x-ray image detectors. The bimolecular-recombination-limited charge collection efficiency depends on the total photogenerated carrier density rather than on its spatial distribution. It is found that the recombination plays practically no role up to the total instantaneous carrier generation Q0 of 109EHPs∕cm2 at the applied electric field of 10V∕μm. The effect of recombination on charge collection increases with decreasing applied electric field strength. For high carrier generation (e.g., Q0 of 1012EHPs∕cm2 for an applied field of 10V∕μm), the electric field distributions vary widely across the photoconductor thickness during the travel of charge carriers towards the electrodes. However, the effect of bimolecular recombination on charge collection efficiency is almost independent of bias polarity and the field distribution. The numerical results are also compared with recent experimental data available in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".