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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".