Auroral spectral estimation with wide-band color mosaic CCDs
Bibliographic record
Abstract
Abstract. Optical aurora can be structured over a wide range of spatial and temporal scales with spectral features that depend on the energy of precipitating particles. Scientific studies typically combine data from multiple instruments that are individually optimized for spatial, spectral, or temporal resolution. One recent addition combines all-sky optics with color mosaic CCD (charge-coupled device) detectors that use a matrix of different wide-band micro-filters to produce an image with several (often three) color channels. These devices provide sequences of two dimensional multispectral luminosity with simultaneous exposure of all color channels allowing interchannel comparison even during periods with rapidly varying aurora. At present color auroral image data are primarily used for qualitative analysis. In this study a quantitative approach based on Backus–Gilbert linear inversion was used to better understand the effective spectral resolution of existing and proposed instruments. Two spectrally calibrated commercial detectors (Sony ICX285AQ and ICX429AKL) with very different color mosaics (RGB (red, green, blue) vs. CYGM (cyan, yellow, green, magenta)) were found to have very similar spectral resolution: three channels with FWHM (full-width half-maximum) ≈100 nm; a NIR (near infrared) blocking filter is important for stabilizing inversion of both three-channel configurations. Operating the ICX429AKL in a noninterlaced mode would improve spectral resolution and provide an additional near infrared channel. Transformations from arbitrary device channels to RGB are easily obtained through inversion. Simultaneous imaging of multiple auroral emissions may be achieved using a single-color camera with a triple-pass filter. Combinations of multiple cameras with simple filters should provide ~50 nm resolution across most of the visible spectrum. Performance of other instrument designs could be explored and compared using the same quantitative framework.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".