First on-sky results, performance, and future of the HiCIBaS-LOWFS
Bibliographic record
Abstract
HiCIBaS-LOWFS is a spatially modulated pyramid wavefront sensor to be used on the HiCIBaS project, a high-contrast imaging balloon borne telescope, as a fine pointing and atmospheric turbulence sensor. Since the project will be using a relatively small telescope on a limited budget, creative solutions must be developed to respond to the requirements for such systems. For example, we need a linear response to large error in order to be able to correct for pointing error in a photon-limited regime caused by the telescope small size. Most solutions aren't well suited for the optical design in HiCIBaS since the high-contrast coronagraph and the Low-Order Wavefront Sensor (LOWFS) both run as separate instruments. The design is centered around the modification of existing pyramid wavefront sensor by adding static, spatial modulation to an otherwise unmodulated system. The spatial modulation is achieved by adding an axicon (a conical optical element) at an imaged telescope pupil plane. This has for effect to add a very large non- common path aberration between the imaging plane and the wavefront sensor. This has for effect to shape the point-spread function incident on the pyramid to a ring shape, which minimize diffraction effect on the apex of imperfect pyramids. We present the first lab results involving the wavefront sensor and its performances for wavefront reconstruction and pointing accuracy. We also discuss the first on-sky results that were recorded with the 1.6-m telescope at the Observatoire du Mont-Megantic in Qubec, Canada using Universite Lavals optical AO test-bench. These results pave the way to the design and integration of the wavefront sensor in the context of the HiCIBaS project.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".