Converting TREx-RGB green-channel data to 557.7 nm auroral intensity: Methodology and initial results
Bibliographic record
Abstract
The recently deployed Transition Region Explorer (TREx) RGB (red-green-blue) all-sky-imager (ASI) is designed to capture 'true color' images of the aurora and airglow. Since the 557.7 nm green-line is usually the brightest emission line in visible auroras, the green-channel of a TREx-RGB camera is usually dominated by the 557.7 nm emission. Under this rationale, the TREx mission does not include a specific 557.7 nm imager, and is designed to use the RGB green-channel data as a proxy of the 557.7 nm aurora. In this study, we present an initial effort to establish the conversion ratio/formula linking the RGB green-channel data to the absolute intensity of 557.7 nm auroras, which is crucial for quantitative uses of RGB data. We illustrate two approaches: (1) through a comparison with the collocated measurement of green-line auroras from TREx spectrograph; (2) through a comparison with the modeled green-line intensity according to realistic electron precipitation flux measurements from low-Earth-orbit satellites, with the aid of an auroral transport model. We demonstrate the procedures and provide initial results for the TREx RGB ASIs at Rabbit Lake and Lucky Lake stations. The RGB response is found to be nonlinear. Empirical conversion ratios/formulas between RGB Green-channel data and the green-line auroral intensity are given and can be immediately applied by TREx RGB data users. The methodology established in this study will also be applicable to the upcoming SMILE ASI mission, which will adopt a similar RGB camera system in its deployment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".