Bibliographic record
Abstract
Silicon Photo-Multipliers (SiPMs) have emerged as a compelling photo-sensor solution over the course of the last decade. SiPMs consist of an array of tightly packed microcells with each microcell acting as an avalanche photodiode that can behave in the Geiger mode regime when the device is reverse biased above a threshold voltage (breakdown voltage). In contrast to the widely used Photomultiplier Tubes (PMTs), SiPMs are low voltage powered, optimal for operation at cryogenic temperatures, and have low radioactivity levels with high gain stability over the time in operational conditions. For these reasons, large-scale low-background cryogenic experiments, such as DarkSide and nEXO plan to use this type of photodetector for their dark matter and neutrinoless double beta decay search. Despite their excellent Photon Detection Efficiency (PDE) SiPMs suffer however from Dark and correlated avalanche noise (After Pulse and Cross-Talk) that can reduce their pulse counting characteristics. In order to optimize the performances of new generation of SiPMs and to understand the noise characteristics of the existing ones in this talk we will propose a new physics motivated model to explain the over voltage dependence of the SiPM nuisance processes i.e. Dark Noise, After Pulse and Cross-Talk. The starting point is the extraction of the electron and hole avalanche triggering probabilities. Then we show that we can describe the over-voltage dependence of these three processes using a minimum set of parameters. In particular we will explain how it is possible to use the over voltage dependence to discriminate the type of carrier that initialise the avalanche and therefore extract the relative contribution of electrons vs holes in p-on-n SiPMs. In the talk we will show preliminary results for the characterization of the Hamamatsu VUV4 MPPC one Fondazione-Bruno-Kessler (FBK) SiPM.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".