Improved telescope focus using only two focus images
Bibliographic record
Abstract
In an effort to reduce the amount of time spent focusing the telescope and to improve the quality of the focus, a new procedure has been investigated and implemented at the Canada-France-Hawaii Telescope (CFHT). The new procedure is based on a paper by Tokovinin and Heathcote and requires only two out-of-focus images to determine the best focus for the telescope. Using only two images provides a great time savings over the five or more images required for a standard through-focus sequence. In addition, it has been found that this method is significantly less sensitive to seeing variations than the traditional through-focus procedure, so the quality of the resulting focus is better. Finally, the new procedure relies on a second moment calculation and so is computationally easier and more robust than methods using a FWHM calculation. The new method has been implemented for WIRCam for the past 18 months, for MegaPrime for the past year, and has recently been implemented for ESPaDOnS.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".