Target and science visibility of the solar-terrestrial observer for the response of the magnetosphere (STORM) global imaging mission concept
Bibliographic record
Abstract
Imaging missions in Earth Science, Heliophysics, and Astrophysics have made fundamental advancements in science and have helped to further our understanding of our natural environment. Here we review the Solar-Terrestrial Observer for the Response of the Magnetosphere (STORM) mission concept, a global solar wind-magnetosphere imaging mission and investigate how often STORM can observe and image its key science targets; the magnetopause, ring current, and auroral oval. We introduce a novel analysis which defines STORM’s plasma targets as discrete sample points in space, these points are collectively called point groups. These point groups are used in conjunction with fields-of-view of STORM’s imagers to quantify target visibility, how often the mission can observe each of its targets. The target visibility is combined with a statistical investigation of historical solar wind and geomagnetic data, and a k-folds/Monte Carlo analysis to quantify STORM’s science visibility. That is how often specific targets can be observed during elevated solar wind and geomagnetic conditions such that detailed science investigations can be completed to address STORM’s science objectives. This analysis is further expanded to potential dual-spacecraft mission configurations to determine the nominal inter-orbit phasing which maximizes target and science visibility. Overall, we find that the target and science visibility of a single spacecraft mission is large, in the 100s and 1000s of hours/events, while the target and science visibility peak for a dual-spacecraft mission where the two spacecraft are ∼85○ out of phase.
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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 | 0.003 |
| 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 | 0.000 | 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".