Multiplexing Approaches for a 12 x 4 Array of Silicon Photomultipliers
Bibliographic record
Abstract
Two resistor network multiplexing circuits for a 12 × 4 array of SiPMs were constructed and tested. Both circuits encode the position and energy information from 48 SiPM pixels in only 4 analog channels. The two circuits differ in that one buffers each SiPM output with a non-inverting voltage-feedback operational amplifier before multiplexing, whereas the second one connects the output of the SiPMs directly to a charge division resistor network. The energy and timing resolution were measured with a 4 × 4 array of LYSO scintillator crystals with size matched to the SiPM pixel size. The measurement was done in 3 steps to cover all 12 × 4 SiPMs. Both circuits gave an energy resolution of 14%. The single sided timing resolution for the buffered output circuit was 2.86 ns, using a 350-650 keV energy window. In comparison, the timing for the circuit with direct connections between SiPMs and the resistor network was 3.54 ns, using the same energy window. Based on these results, the predicted coincidence timing resolutions are 4.0 ns and 5.0 ns, respectively. The coupling of the SiPM capacitance with the resistor network results in different signal shaping time constants for different SiPMs in the passive resistor network, causing a delay in trigger time for the inner SiPM signals. On the other hand, the circuit with buffer amplifiers does not suffer from this effect, and the pulse shape is more uniform across the SiPMs. We also demonstrate, using a 10 × 10 array of 1.5 mm LYSO crystals, that the inclusion of multiple SiPMs in both circuits reduces the detector's ability to resolve crystals in the flood histograms. The amount of noise increases with number of SiPMs in the multiplexing circuit.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".