Characterization of silicon photomultipliers for their application in muon scattering tomography
Bibliographic record
Abstract
Abstract Muon scattering tomography is a non-destructive technique used to image different materials by utilizing natural cosmic ray muons. Typically it requires position-sensitive detectors with a sub-millimeter resolution to effectively distinguish high-Z materials in a compact system. The plastic scintillating fiber detector is a feasible candidate and is currently being designed with one-dimensional silicon photomultiplier (SiPM) readout. In this work, we constructed experimental setups to characterize three different SiPMs from the NDL, SensL, and HPK manufacturers for optimal performance of the scintillating fiber detector. The breakdown voltage, temperature compensation factor, dark noise, and photodetection efficiency of each SiPM are evaluated and summarized. Among the SiPMs tested, the HPK SiPM demonstrated the lowest dark count rate and crosstalk probability while exhibiting the best photodetection efficiency response at the emission wavelengths of the scintillating fibers. This makes the HPK SiPM particularly well-suited to meet the requirements of the detector and serves as a reference for further customization of the one-dimensional SiPM array.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".