X-ray interaction-induced signal and noise performances of edge-on silicon microstrip detectors for digital mammography
Bibliographic record
Abstract
We are developing a silicon microstrip-array detector, which is operated in photon-counting mode, for line-scanned digital mammography. To enhance the x-ray interaction efficiency, the x-ray beam is oriented toward the edge of microstrips, known as the edge-on geometry. To predict the fundamental signal and noise performances induced by x-ray interactions, we performed Monte Carlo simulations. Absorbed energy distribtuions were obtained for various tilting angles (5 to 85 degrees) in the edge-on detector geometry for a wide range of incident energies from 1 to 50 keV. Based on the energy-moments theory with the obtained absorbed energy distributions, we estimated various physical performance parameters such as the quantum absorption efficiency, the average energy deposition per interaction, and the Swank noise factor. In addition, relative accuracy and imprecision in photon-energy measurements were estimated. These analyses were extended to the typical poly-energetic mammography x-ray spectra from various target materials such as molybdenium, rhodium and tungsten. Among performance parameters, the quantum absorption efficiency was gradually decreased as the tilting angle increases because of the reduction in pathlength where x-ray photons travel, while others were almost insensitive to the tilting angle. The best signal and noise performances in the edge-on silicon microstrip detector were obtained for the rhodium spectrum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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 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".