Fast camera objective designs for spectrograph of Mont Megantique telescope
Bibliographic record
Abstract
All-reflective optics is conventionally required for extended spectral region observations in astronomical spectrograph. But the spatial resolution is usually not good enough while the large-size CCD will be used for observation in all-reflective optics. In this paper, all-refractive optics has been investigated to design a fast (F/1.55) and wide angle camera objective for large spectral coverage, from UV to VIS and up to NIR, when a large-size CCD is used on the focal plane of the spectrograph of Mont Megantique telescope. The case of achromatic and apochromatic condition has been investigated for axial and lateral color controls. The new proposed solutions have been optimized from two to three different glass combinations in order to have higher throughputs for large spectral coverage, especially in UV region. The used components have been minimized to reduce the light inherent lost. The monochromatic aberrations have been corrected and controlled by using optimized lens bending and shapes to make the camera have the CCD pixel resolution. Ray tracing results displayed the good optical performance of the camera to cover from 350 nm to 1000 nm spectral region with high resolution. The broadband AR coating, enhanced on UV region, will be used on each surface of the lenses in the camera. Final throughputs for the designed camera has been estimated and given in the paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".