P oS(PD07)026 Compact, Low-power and Precision Timing Photodetector Readout
Bibliographic record
Abstract
Photodetector readout for next generation high event rate particle identification and single-photon detection requires a digitizer capable of integrated recording of dense arrays of sensor elements with high analog bandwidth (precision timing) and large record depth, in a cost-effective, compact and low-power way. Simply stated, one cannot do better than having a high-fidelity “oscilloscope on a chip ” for every sensor channel. A first version of the Buffered Large Analog Bandwidth (BLAB1) ASIC has been designed based upon the lessons learned from the development of the Large Analog Bandwidth Recorder and Digitizer with Ordered Readout (LABRADOR) ASIC. While this LABRADOR ASIC has been very successful and forms the readout basis of a genera-tion of new, large-scale radio neutrino detectors, its limited sampling depth is a major drawback. To address this shortcoming, a prototype intended for photodetector readout has been designed and fabricated with 64k deep sampling at multi-GSa/s operation. An evaluation system has been constructed for instrumentation of Time-Of-Propagation (TOP) and focusing DIRC prototypes and test results will be reported. International workshop on new photon-detectors PD07
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".