First on-sky results, performance, and future of the HiCIBaS-LOWFS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it