High-Contrast Imaging Performance of a Tunable Filter for Space-Based Applications. II. Detection and Characterization Capabilities
Bibliographic record
Abstract
The scanning capability of a tunable filter represents an attractive option for performing high-contrast observations through spectral differential imaging (SDI), a speckle-attenuation 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 ). TFI features a low-order Fabry-Perot etalon enabling imaging spectroscopy at an average resolution of 100 in the 1.5 to 5 μm range. TFI also includes a high-contrast imaging mode featuring a Lyot coronagraph aided by SDI. TFI's on-sky performance is determined by performing an end-to-end Fresnel propagation of the telescope and instrument using the measured wavefront error maps of TFI's optical elements and the theoretical wavefront error maps of the optical telescope assembly. Using this simulation, we determine that SDI offers an improvement in contrast ranging from a factor of ∼7 to ∼100, depending on the instrument's configuration. We present the companion detection capability using both the coronagraphic and noncoronagraphic modes of TFI and demonstrate the characterization capability using the HR 8799 and Fomalhaut systems. The performance of roll subtraction is also determined and compared with that of SDI. We also present the SDI capability of the Near-Infrared Imager and Slitless Spectrograph, the science instrument module to replace TFI in the JWST Fine Guidance Sensor.
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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 | 0.000 |
| 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".