Measuring count rates free from correlated noise in digital silicon photomultipliers
Bibliographic record
Abstract
Abstract The characterization of nuisance parameters in digital silicon photomultipliers (SiPMs) is important to their understanding and future development. Methods able to distinguish the types of events are necessary to obtain fair and legitimate measurements. In this work, the zero photon probability (ZPP) method and the time delay (TD) method are used to measure the dark noise of digital SiPMs free from the contribution of correlated noise such as afterpulsing and crosstalk. It highlights the unique features of digital SiPMs such as the holdoff delay, the digital output signal, and the embedded processing (e.g. the selection of the interval sampling width). The two methods correctly separate the correlated and uncorrelated events in digital SiPMs and therefore the determination of a true photon detection efficiency (PDE) is possible. The ZPP method is also implemented inside a digital SiPM using embedded digital signal processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".