Tilted pressure-tuned field-widened Michelson interferometer for high spectral resolution lidar
Bibliographic record
Abstract
High spectral resolution lidars (HSRLs) designed for aerosol and cloud remote sensing are increasingly being deployed on aircraft and called for on future space-based missions. The HSRL technique relies on spectral discrimination of the atmospheric backscatter signals to enable independent, unambiguous retrieval of aerosol extinction and backscatter. NASA Langley Research Center is developing a tilted pressure-tuned field-widened Michelson interferometer (FWMI) to achieve the spectral discrimination for an HSRL system. The FWMI consists of a cubic beam splitter, a solid glass arm, and a sealed air arm. The spacer that connects the air arm mirror to the main part of the interferometer is designed to minimize thermal sensitivity. The pressure of the sealed air-arm air can be accurately controlled such that the frequency of maximum interference can be tuned with great precision to the transmitted laser wavelength. In this paper, the principle of the tilted pressure-tuned FWMI for HSRL is presented. The pressure tuning rate, the tilted angle requirement and challenges in building the real instrument are 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 | 0.001 |
| 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 | 0.001 |
| 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".