Modeling coherence effects on cavity-based spectroscopy
Bibliographic record
Abstract
Optical cavities are powerful instruments with which to perform sensing experiments. Through spectroscopic techniques, the presence of a target biological analyte or chemical species can be inferred by detecting the changes they produce in the spectral properties of a cavity. Conventional cavity-based spectroscopy relies on very narrow linewidth laser sources in order to mitigate the effects of optical coherence on the measurements they yield. The growing demand for fully-integrated and miniaturized sensing platforms for purposes such as point-of-care testing, disaster management, and environmental monitoring places stringent constraints on the design of sensing instruments. As a result, the use of optical sources with larger linewidths is unavoidable for these applications. Given this context, the aim of this work is the derivation of closed-form analytic models of cavity-based sensing that quantify the impact of source bandwidth (i.e., coherence) on spectroscopic measurements. Three cavity-based sensing paradigms are investigated in detail: cavity-enhanced absorption spectroscopy (CEAS), cavity ring-down spectroscopy (CRDS), and phase-shift cavity ring-down spectroscopy (PS-CRDS). Furthermore, examples based on a microtoroidal cavity are presented for each paradigm, where predicted measurements obtained from systems using tunable laser (narrow linewidth) or laser diode (large bandwidth) sources are compared. The models developed in this work demonstrate that microtoroid systems employing tunable lasers can detect changes in cavity transmission coefficient and refractive index. Laser diodes also yield measurements that are comparably sensitive to changes in cavity transmission coefficient, but they are insensitive to the microtoroid's refractive index.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".