Pupil-plane LLOWFS simulation and laboratory results from NEW-EARTH’s high-contrast imaging testbed
Bibliographic record
Abstract
Direct imaging of exoplanets can be used to characterize exoplanets by spectroscopy of their atmospheres. Coronagraphs are required to suppress the diffraction effects by blocking the starlight, however, residual wavefront error scatters starlight in the science images, losing faint exoplanet photons in stellar noise. The performance of a coronagraphic system is thus contingent upon how efficiently the wavefront aberrations are minimized. Lyot-stop low-order wavefront sensor (LLOWFS) is a well-established sensor that senses the light rejected by the focal plane mask and corrects low-order aberrations upstream of the coronagraph. Previous versions of the LLOWFS sensed the residual starlight at the defocused focal plane. However, on the NRC's NEW-EARTH high-contrast imaging testbed, pupil-plane images of LLOWFS have been used to address both Zernike and Fourier modes. The goal of the testbed is to develop SPIDERS/Subaru which is the technology demonstrator of the CAL2 unit of the upcoming Gemini Planet Imager 2.0 (GPI 2.0). Both SPIDERS and CAL2 will address the low-order modes for stabilizing speckles, and demonstrate an active suppression of speckles using the Fast Atmospheric Self-Coherent Camera Technique (FAST) by creating a region of up to 10-7 contrast at small angles. Thus, obtaining sub-nanometric pointing stability using the LLOWFS is crucial for achieving stable contrast results on the bench and on-sky. Here, we present LLOWFS closed-loop laboratory results under simulated post-Adaptive Optics residuals of GPI 2.0 and simulations of the LLOWFS and FAST sensors for SPIDERS.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".