Exoplanet detection yield of a space-based Bracewell interferometer from small to medium satellites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it