High-Contrast Imaging Performance of a Tunable Filter for Space-based Applications I: Laboratory Performance
Bibliographic record
Abstract
The scanning capability of a tunable filter represents an attractive option for performing high-contrast imaging through spectral differential imaging (SDI), a speckle-suppression technique widely used by current ground-based high-contrast imaging instruments. The performance of such a tunable filter is illustrated through the Tunable Filter Imager (TFI), which used to be part of the science instrument complement of the James Webb Space Telescope ( JWST ). The TFI features a low-order Fabry-Perot etalon that enables imaging spectroscopy at an average resolving power of 100. Also included is a high-contrast imaging mode featuring a Lyot coronagraph aided by spectral differential imaging (SDI). Using a TFI prototype etalon, we demonstrate the calibration technique to be used in the parallelization of the etalons reflective plates and then evaluate the etalon's ability to perform speckle suppression through SDI. The improvement in contrast ranges from a factor of ∼10 at working angles greater than 11λ/ D , increasing up to a factor of ∼60 at 5λ/ D . These results are consistent with a Fresnel optical propagation model, which we use to show that the contrast improvement is limited by the test bed, and not the etalon. Our results demonstrate that a tunable filter such as the JWST TFI is an attractive solution for performing speckle suppression in space through multiwavelength imaging.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".