Improved coupling to integrated spatial heterodyne spectrometers with applications to space
Bibliographic record
Abstract
Multiple Aperture Transform Chip Heterodyne (MATCH) spectrometers have been developed for targeted remote sensing applications in harsh environments. These waveguide-based Fourier Transform Spectrometers (FTS) offer significant improvements in resource efficiency over monolithic glass implementations, but are relatively limited in terms of input coupling efficiency and fill factor of the input facet. Integrated optics spectrometers have significant resource advantages for space applications. Monolithic Spatial Heterodyne Spectrometers are insensitive to vibration and do not require frequent calibration. In addition, Fourier Transform Spectrometers are known to provide significant performance advantages for emission spectroscopy. Ongoing work will improve the MATCH spectrometer input coupling efficiency from free space. This paper discusses the signal to noise improvements expected by incorporation of surface gratings, or back-thinning and stacking of slabs. We show that the use of surface gratings can increase the throughput over coupling to bare waveguides alone (in a single polarization), and provide close to 100% fill factor, albeit with limited field. Étendue improvements associated with stacked slabs are limited only by the sensing area available, but the fill factor of the input facet is limited to ~10%. The impact of these improvements is assessed in the context of two space-based applications: 1) Atmospheric remote sensing in the context of Spatial Heterodyne Observations of Water (solar occultation absorption spectroscopy) near 1.3 μm and 2) Point emission spectroscopy (LIBS/Raman/fluorescence) for mineral identification on a planetary rover.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".