An optical feasibility study for STARLITE: superluminous tomographic atmospheric reconstruction with laser-beacons for imaging terrestrial exoplanets
Bibliographic record
Abstract
Detecting and characterizing Earth-size exoplanets by imaging around Sun-like stars to search for life signatures is a monumental challenge, requiring advanced coronagraph designs and picometer-level wavefront control to achieve 1010 contrast at separations of only a few lambda/D. While a dedicated space observatory may be required to achieve such extreme observations, a new concept is presented to achieve an intermediate sky-limited 108 visible-band contrast using ground-based 8 to 30m class telescopes. STARLITE (Superluminous Tomographic Atmospheric Reconstruction with Laser-beacons for Imaging Terrestrial Exoplanets) consists of a satellite constellation located on a highly-elliptical, 350, 000km apogee orbit that will allow ∼hours-long observations of astronomical targets. When the constellation surrounds a target, the onboard laser on each satellite will generate extremely bright unresolved off-axis guide stars for adaptive optics, with an exoplanet imaging goal to reach closed-loop Angstrom-level residuals on a science target. In this paper I will present preliminary end-to-end simulations of a five laser-beacon optical system through the atmosphere to estimate the wavefront reconstruction accuracy towards a science target.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".