Lock-in common-mode rejection demodulation: Measurement technique and applications to thermal-wave detection: Theoretical
Bibliographic record
Abstract
The ability of conventional single-ended photothermal techniques to detect weak inhomogeneities in a given material is mainly limited by two instrumental factors: the signal-to-noise ratio (SNR) and the amplitude dynamic range. The amplitude level is limited by the output signal baseline, and may be too high to monitor relatively small variations introduced by the presence of weak inhomogeneities. The purpose of this work is to introduce a novel photothermal signal generation methodology, the principle of which can be broadly applied to any technique utilizing a lock-in analyzer demodulation scheme of periodic signal wave forms. Unlike the conventional single-ended periodic excitation wave form, which uses a 50% duty-cycle square wave or sinusoidal modulation of the pump laser heating beam, a more complicated periodic modulation wave form is employed, resulting in the equivalent of differential-signal demodulation. The new wave form takes advantage of the real-time differential action performed by the lock-in amplifier weighing function over the two half periods of the modulated signal. This results in enhanced signal dynamic range due to the efficient suppression of the baseline and a substantial improvement in the SNR. The main features of this technique are investigated with a theoretical model for an arbitrary repetitive signal wave form and, in particular, for a photothermal signal. The dependence of the signal on the wave form parameters is also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".