A new approach to the determination of the Fano factor for semiconductor detectors
Bibliographic record
Abstract
Abstract Values of the Fano factor are widely scattered in the literature, indicating the difficulties in its determination. We chose to analyze both the escape peaks and parent peaks of Ge detector spectra where the parent peak penetration depth and the escape peak escape depth are much larger than the size of the incomplete charge collection region. Hence, the escape peaks are expected to be free of low‐energy tailing as, although the actually deposited energy is low, it is deposited beyond the incomplete charge collection region. It was found that the product of electron–hole pair creation energy (ε) and the Fano factor ( F ) has an energy dependence at low energies, as is expected from electron transport theories. Although ε is expected to have its own energy dependence, if a reference value of 2.96 eV is assumed for ε, then the Fano factor values varied between 0.059 and 0.083. The escape peak is less advantageous for Si, hence a different method was used for Si(Li) detectors. Assuming the reference value of 3.8 eV for ε, the Fano factor was found to be 0.063 at 5.9 keV x‐ray energy. We consider the Fano factors reported here as upper limits, rather than the mean values. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".