Bibliographic record
Abstract
Test and characterization of the optical performance for the novel and advanced sub-optical-systems, as designed for large astronomical optical telescopes, often need to be carried out with the simulated telescope wavefront in the labs before working with the telescopes. Computer generated hologram (CGH) is a simple solution for monochromatic wavefront generation. Severely off-axis aberrated wavefront coming from the one-mirror or two-mirrors telescope can be generated by CGH with symmetric and un-symmetric terms of the Zernike polynomials. The chrome mask and/or the binary CGHs have been made as the CFH telescope simulator. The unused diffraction orders of the CGH can be completely eliminated by adding the carrier frequency on the phase function of the designed CGHs. The test results that are given with the fabricated CGH simulator have a good agreement with that of the simulation. The aberrated wavefront of the simulated telescope can be also generated by the ferrofluid deformable mirror (FDM) at low cost. A 100-mm FDM is designed and built with 271 actuators for performance evaluation. The revolution symmetric aberrated wavefront has been experimentally generated and proven by the FDM prototyping. The simulation and test results for the prototyping FDM are both given in this paper.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".