Performance characterization of a pressure-tuned wide-angle Michelson interferometric spectral filter for high spectral resolution lidar
Bibliographic record
Abstract
High Spectral Resolution Lidar (HSRL) is typically realized using an absorption filter to separate molecular returns from particulate returns. NASA Langley Research Center (LaRC) has designed and built a Pressure-Tuned Wide-Angle Michelson Interferometer (PTWAMI) as an alternate means to separate the two types of atmospheric returns. While absorption filters only work at certain wavelengths and suffer from low photon efficiency due to light absorption, an interferometric spectral filter can be designed for any wavelength and transmits nearly all incident photons. The interferometers developed at LaRC employ an air spacer in one arm, and a solid glass spacer in the other. Field widening is achieved by specific design and selection of the lengths and refractive indices of these two arms. The principal challenge in using such an interferometer as a spectral filter for HSRL aboard aircraft is that variations in glass temperature and air pressure cause changes in the interferometer’s optical path difference. Therefore, a tuning mechanism is needed to actively accommodate for these changes. The pressure-tuning mechanism employed here relies on changing the pressure in an enclosed, air-filled arm of the interferometer to change the arm’s optical path length. However, tuning using pressure will not adjust for tilt, mirror warpage, or thermally induced wavefront error, so the structural, thermal, and optical behavior of the device must be well understood and optimized in the design and manufacturing process. The PTWAMI has been characterized for particulate transmission ratio, wavefront error, and tilt, and shows acceptable performance for use in an HSRL instrument.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".