Resolved imaging of exoplanets with the solar gravitational lens
Bibliographic record
Abstract
ABSTRACT We discuss the feasibility of direct multipixel imaging of exoplanets with the solar gravitational lens (SGL) in the context of a realistic deep space mission. For this, we consider an optical telescope, placed in the image plane that forms in the strong interference region of the SGL. We consider an Earth-like exoplanet located in our immediate stellar neighbourhood and model its characteristics using our own Earth. We estimate photon fluxes from such a compact, extended, resolved exoplanet. This light appears in the form of an Einstein ring around the Sun, seen through the solar corona. The solar corona background contributes a significant amount of stochastic noise and represents the main noise source for observations utilizing the SGL. We estimate the magnitude of this noise. We compute the resulting signal-to-noise ratios (SNRs) and related integration times that are needed to perform imaging measurements under realistic conditions. It is known that deconvolution, removing the blur due to the SGL’s spherical aberration substantially decreases the SNR. Our key finding is that this ‘penalty’ is significantly mitigated when sampling locations in the image plane (image pixels) remain widely spaced. Consequently, we conclude that an imaging mission is challenging but feasible, using technologies that are either already available or in active development. Under realistic conditions, high-resolution imaging of Earth-like exoplanets in our galactic neighbourhood requires only weeks or months of integration time, not years as previously thought: a high quality 1000 × 1000 pixel image of an Earth-like planet at Proxima Centauri could be obtained with SNR > 10 using approximately 14 months of integration time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".