Characterization of SiPM Avalanche Triggering Probabilities
Bibliographic record
Abstract
Silicon photo-multipliers (SiPMs) are detectors sensitive to single photons that are used to detect scintillation and Cherenkov light in a variety of physics and medical-imaging applications. SiPMs measure single photons by amplifying the photo-generated carriers (electrons or holes) via a Geiger-mode avalanche. The photon detection efficiency (PDE) is the combined probability that a photon is absorbed in the active volume of the device with a subsequently triggered avalanche. Absorption and avalanche triggering probabilities are correlated since the latter probability depends on where the photon is absorbed. In this article, we introduce a physics-motivated parameterization of the avalanche triggering probability that describes the PDE of a SiPM as a function of its reverse bias voltage, at different wavelengths. This parameterization is based on the fact that in p-on-n SiPMs, the induced avalanches are electron-driven in the ultraviolet (UV) range, while they become increasingly hole-driven toward the infrared range. The model has been successfully applied to characterize two Hamamatsu multi-pixel photon counters (MPPCs) and one Fondazione-Bruno-Kessler (FBK) SiPM, and it can be extended to other SiPMs. Furthermore, this model provides key insight into the electric field structure within SiPMs, which can explain the limitation of the existing devices and be used to optimize the performance of the future SiPMs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".