Adaptive optics with an infrared pyramid wavefront sensor at Keck
Bibliographic record
Abstract
The study of cold or obscured, red astrophysical sources can significantly benefit from adaptive optics (AO) systems employing infrared (IR) wavefront sensors. One particular area is the study of exoplanets around M-dwarf stars and planet formation within protoplanetary disks in star-forming regions. Such objects are faint at visible wavelengths but bright enough in the IR to be used as a natural guide star for the AO system. Doing the wavefront sensing at IR wavelengths enables high-resolution AO correction for such science cases, with the potential to reach the contrasts required for direct imaging of exoplanets. To this end, a new near-infrared pyramid wavefront sensor (PyWFS) has been added to the Keck II AO system, extending the performance of the facility AO system for the study of faint red objects. We present the Keck II PyWFS, which represents a number of firsts, including the first PyWFS installed on a segmented telescope and the first use of an IR PyWFS on a 10-m class telescope. We discuss the scientific and technological advantages offered by IR wavefront sensing and present the design and commissioning of the Keck PyWFS. In particular, we report on the performance of the Selex Avalanche Photodiode for HgCdTe InfraRed Array detector used for the PyWFS and highlight the novelty of this wavefront sensor in terms of the performance for faint red objects and the improvement in contrast. The system has been commissioned for science with the vortex coronagraph in the NIRC2 IR science instrument and is being commissioned alongside a new fiber injection unit for NIRSPEC. We present the first science verification of the system—to facilitate the study of exoplanets around M-type stars.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".