Exoplanet detection yield of a space-based Bracewell interferometer from small to medium satellites
Bibliographic record
Abstract
Space-based nulling interferometry is one of the most promising solutions to spectrally characterize the atmosphere of rocky exoplanets in the mid-infrared (3 to 20 μm). It provides both high angular resolution and starlight mitigation. This observing capability depends on several technologies. A CubeSat (up to 20 kg) or a medium satellite (up to a few hundreds of kg), using a Bracewell architecture on a single spacecraft could be an adequate technological precursor to a larger, flagship mission. Beyond technical challenges, the scientific return of such a small-scale mission needs to be assessed. We explore the exoplanet science cases for various missions (several satellite configurations and sizes). Based on physical parameters (diameter and wavelength) and thanks to a state-of-the-art planet population synthesis tool, the performance and the possible exoplanet detection yield of these configurations are presented. Without considering platform stability constraints, a CubeSat (baseline of b ≃ 1 m and pupils diameter of D ≃ 0.1 m) could detect ≃7 Jovian exoplanets, a small satellite (b ≃ 5 m / D ≃ 0.25 m) ≃120 exoplanets, whereas a medium satellite (b ≃ 12.5 m / D ≃ 0.5 m) could detect ∼250 exoplanets including 51 rocky planets within 20 pc. To complete our study, an analysis of the platform stability constraints (tip/tilt and optical path difference) is performed. Exoplanet studies impose very stringent requirements on both tip/tilt and OPD control.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".