<title>Preamplifier impulse-response shape-driven shot-noise in direct-detection photon-counting laser radars</title>
Bibliographic record
Abstract
The number of photons returning form a target in a given time interval is well described by a negative-binomial distributed random variable. A photomultipler tube (PMT) photon-counting detector is optimal for direct detection, and the number of detected-photon 'electron pulses' produced is also negative-binomially distributed per time bin, with a reduced mean due to the device quantum efficiency. These time distributed electron pulses are amplified and filtered by the preamplifier electronics prior to digitization and signal processing. The voltage output pulse per individual photo-electron event is known as the 'impulse-response- function' of the detector and preamplifier. In this study we employ a typical analog preamplifier filter response, modeled as a Butterworth lowpass filter of order two, which filters a 200 ps wideband PMT input voltage pulse. The random summation of these lowpass voltage impulse-responses, as created by the negative-binomial photon arrival times and random photo-electron creation, is the classical electronic 'shot-noise' random process. We derive numerically the voltage probability density function of this negative- binomial/impulse-response driven shot-noise random process following the stochastic process literature. We also show a technique to include PMT variations in gain, known as the 'pulse height distribution,' and to incorporate Gaussian baseline-noise voltage. Agreement with AMOR experiments is shown to be excellent. In addition, a Monte Carlo realization is presented, using the same impulse-response temporal shape, which also gives excellent agreement with AMOR data and with the analytical/numerical calculations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".