Fast catadioptric optics with large field of view
Bibliographic record
Abstract
High resolution is in demand for the new applications based on the use of infrared technology. For observation task, high resolution provides more information either under the form of better resolving power or larger field-of-view. Various solutions can be envisioned to achieve high resolution imaging. In this paper, a combination of high resolution detector and microscanning system is proposed. This strategy results in higher resolution and reduced aliasing. A catadioptric configuration is preferred when a microscan is required to increase the spatial sampling frequency. Among the catadioptric configurations, the Schmidt-Cassegrain has wide angle capability due to its aspherical entrance window. However, when the system is used in harsh environment, this compensator window may have to be replaced often. In this case, a flat window would be preferred because it can be removed or easily replaced at reasonable cost. The reduction of the aberrations to an acceptable level without compensator window requires that the mirrors of the telescope be aspherized. In this paper, we present a modified Cassegrain telescope with two aspherical mirrors and one field lens. Due to the large obscuration of the secondary mirror, the effective F/1.05 necessitates a larger working F-number of 0.75. The spectral band ranges from 7.0 to 14.0 microns and the focal length is 50mm. The system is designed for the ULIS UL04171 microbolometer detector with 640 x 480 pixels and 25 microns pixel pitch. With this sensor, the total field of view of the system is 22.6 degrees, which is very large for a catadioptric system. A microscan increases the system maximal spatial sampling frequency from 20 to 40 cycles per millimeter. Despite of the compactness, there is enough room between the field lens and the detector to insert a shutter. A baffle extending ahead of the device is needed in this large field of view design to avoid undesired rays reaching the detector.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".