A versatile Monte Carlo package for computation of efficiencies of Si(Li), SDD and Ge detectors
Bibliographic record
Abstract
Abstract Currently, most detector efficiency calculations for X‐ray detectors assume that the source is a point source on the axis of symmetry of the detector, but this is not always accurate. We have devised a Monte Carlo program to simulate photon transport in Si(Li), SDD and planar Ge detectors that natively handles finite, tilted and off‐axis sources. Although electron transport is not handled at this stage, photon transport is completely handled, including absorption from filters and multiple scattering in the detector crystal. The K escape peak is handled for both silicon and germanium detectors, and the L escape peak is also handled for germanium detectors. Our efficiency results compare very well with previous work when idealized systems are simulated, and the effect of a non‐idealized system is presented. Escape peak intensity ratios are given for both silicon detectors (K peak only) and germanium detectors (K and L peaks), and the results for the K escape peaks agree well with previous work. Results are presented for a recent annular detector system, which is a good example of systems that are poorly handled under previous efficiency calculations. Copyright © 2010 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".