Spectrally resolved imaging with the solar gravitational lens
Bibliographic record
Abstract
We consider the optical properties of the solar gravitational lens (SGL) treating the Sun as a massive compact body. Using our previously developed wave-optical treatment of the SGL, we convolve it with a thin lens representing an optical telescope, and estimate the power spectral density and associated photon flux at individual pixel locations on the image sensor at the focal plane of the telescope. We also consider the solar corona, which is the dominant noise source when imaging faint objects with the SGL. We evaluate the signal-to-noise ratio at individual pixels as a function of wavelength. To block out the solar light, we contrast the use of a conventional internal coronagraph with a Lyot-stop to an external occulter (i.e., starshade). An external occulter, not being a subject to the diffraction limit of the observing telescope, makes it possible to use small telescopes (e.g., $\ensuremath{\sim}40\text{ }\text{ }\mathrm{cm}$) for spatially and spectrally resolved imaging with the SGL in a broad range of wavelengths from optical to mid-infrared and without the substantial loss of optical throughput that is characteristic to internal devices. Mid-IR observations are especially interesting as planets are self-luminous at these wavelengths, producing a strong signal, while there is significantly less noise from the solar corona. This part of the spectrum contains numerous features of interest for exobiology and biosignature detection. We develop tools that may be used to estimate instrument requirements and devise optimal observing strategies to use the SGL for high-resolution, spectrally resolved imaging, ultimately improving our ability to confirm and study the presence of life on a distant world.
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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 | 0.000 |
| 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 | 0.000 |
| 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".