Ultra-compact remote CO2 detection with silicon waveguide ring resonators (Conference Presentation)
Bibliographic record
Abstract
Astronomical instrumentation is traditionally costly, large, and alignment-sensitive owing to the use of bulk optics. The use of integrated photonic devices in astronomical instrumentation can mitigate such drawbacks in certain applications where high light throughput and spectral bandwidth are less crucial. In this work, we present an ultra-compact carbon dioxide detection scheme using a single silicon waveguide ring resonator. The comb-like absorption line spectrum of CO2 around 1580 nm wavelength can closely match the comb spectrum of an appropriately designed ring resonator. By actively correlating such a ring spectrum with the CO2 absorption lines, a specific detection signal can be generated. We design the free spectral range of a ring resonator to match the absorption line spacing of carbon dioxide lines in the range from 1575 to 1585 nm. Using thermo-optic modulation, the ring resonator drop or through port transmission spectrum can be shifted back and forth across the incoming CO2 light spectrum, resulting in a modulated signal with an amplitude proportional to the CO2 absorption line strength. Furthermore, high frequency modulation and lock-in detection can result in a significant improvement in the signal to noise ratio. We demonstrate that such a device can provide real-time carbon dioxide detection for applications in ground- and satellite-based astronomy, as well as remote atmospheric sensing, in a compact package. In future work, such a sensor can be adapted to a range of gases and used to determine radial velocities and compositional maps of astronomical objects.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".