The relevance of prior inclination determination for direct imaging of Earth-like planets
Bibliographic record
Abstract
Direct imaging and characterization of extrasolar Earth-like planets are strongly impacted by the orbital inclination of the planet to be studied, as a combination of pure geometrical effects and the impact of exozodiacal dust. Here, we perform simulations to quantify the impact of a priori knowledge of inclination for the efficiency of a typical coronagraphic or occulter-based mission. The relative impact and complementarity with prior knowledge of exozodiacal brightness down to achievable levels are examined and discussed. It is found that inclination has an even greater impact than the exozodiacal brightness, though the two have excellent complementarity. We also discuss different methods for inclination determination and their respective applicability to the context of precursor science to an imaging mission. It is found that if technologically achievable, a combined effort to determine inclinations and exozodiacal brightnesses with ground-based facilities would substantially increase the efficiency of a space-based dedicated mission to image and characterize Earth-like planets.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".