High speed multichannel time-correlated single photon counting electronics based on SiGe integrated time-to-digital converters
Bibliographic record
Abstract
Time Correlated Single Photon counting (TCSPC) with picosecond timing is a key method in many areas of applied physics. One of the most important areas is that of fluorescence lifetime measurement in biophysics and the life sciences. Precisely timed photon counting for the purpose of coincidence correlation is now also emerging as the most common approach to quantum state interpretation in experimental quantum optics. Therefore, time-correlated single photon counting electronics, traditionally mostly used in time-resolved fluorescence research are facing new challenges in different emerging areas. Consequently such instruments are undergoing a fresh cycle of innovation, some of which we try to highlight here. The new picosecond TCSPC system we present provides several interesting new features resulting from a high speed monolithic integration in one of the fastest semiconductor technologies available today. The result is a high timing resolution by direct digital conversion and a very short deadtime. Apart from conventional histogramming over a very long time span, we can implement continouos single photon recording modes hat allow picosecond timing of all photon events with respect both to a sync signal as well as on a virtually infinite time scale. Multiple timing channels can also be operated indpendently and in parallel, e.g. for picosecond correlation analysis between signals from multiple photon detectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".