A targeted sparse readout for multi-anode photo-multipliers and optically isolated crystals
Bibliographic record
Abstract
Multi anode photo-multipliers are able to localize the position of a light source, and measure its intensity. Normally these devices have their anode wires connected to a resistor network. If the light originates in an optically collimated, pixilated scintillation crystal array, only a few of the anode wires receive any significant signal. If only those wires whose signal is above the noise threshold are used to localize the light, the signal-to-noise ratio improves (same signal but less noise). We found that only about 6 wires on each axis have a significant signal when a 511 keV gamma ray is detected in a 2 mm/spl times/2 mm BGO crystal. Using a custom made integrated circuit which provides the binary code of the address corresponding to the biggest of 16 analog inputs, and a sample-and-hold on each anode wire we hope to direct a six channel ADC to sample the anode wires which provide the best positioning signal on each axis of the PMT. The shape of the signal envelope permits identification of crystals which would otherwise be beyond the PMT's field of view. This readout should enhance these PMTs in application like positron emission mammography, and small animal PET imaging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".