A SmallSat mission study for STARLITE: superluminous tomographic atmospheric reconstruction with laser-beacons for imaging terrestrial exoplanets
Bibliographic record
Abstract
In the search for life in our galaxy, and for understanding the origins of our solar system, the direct imaging and characterization of Earth-like exoplanets is key. In a step towards achieving these goals, the Superluminous Tomographic Atmospheric Reconstruction with Laser-beacons for Imaging Terrestrial Exoplanets (STARLITE) mission uses five CubeSats in a highly elliptical orbit as artificial guide stars to enable tomographic reconstruction of the atmosphere for extreme multi-conjugate adaptive optics (MCAO). Through the use of current and next-generation extremely-large ground-based telescopes, the STARLITE constellation at its ∼350,000 km apogee can provide brighter than -10 magnitude artificial guide stars from a 10 cm launching telescope in a sub-arcminute field of view for up to an hour. Careful selection and design of the ∼760 nm on-board laser will allow O2 detection and characterization of exoplanet atmospheres. At a size of 12U, each satellite weighs only 19 kg and utilizes mostly commercially available off-the-shelf components to keep costs per satellite around $2M. In this paper, we will present the satellite mission concept and early system design for the STARLITE constellation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".