Wind imaging using simultaneous fringe sampling with field-widened Michelson interferometers
Bibliographic record
Abstract
The first, to our knowledge, successful laboratory implementation of an approach to image winds using simultaneous (as opposed to sequential) fringe imaging of suitable isolated spectral emission lines is described. Achieving this in practice has been a long-standing goal for wind imaging using airglow. It avoids the aliasing effects of source irradiance variations that are possible with sequential fringe sampling techniques. Simultaneous fringe imaging is accomplished using a field-widened Michelson interferometer by depositing phase steps on four quadrants of one of the mirrors and designing an optical system so that four images of the scene of interest, each at a different phase, are simultaneously produced. In this paper, the instrument characteristics, its characterization, and the analysis algorithms necessary for use of the technique for this type of interferometer are described for the first time, to the best of our knowledge. The large throughput associated with field-widened Michelson interferometers is sufficient for the spatial resolutions and temporal cadences necessary for ground based imaging of gravity waves in wind and irradiance to be achieved. The practical demonstration of this technique also validates its use for proposed monolithic satellite instruments for wind measurements using airglow on the Earth and Mars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".