Estimation of normalized point-source sensitivity of segment surface specifications for extremely large telescopes
Bibliographic record
Abstract
We present a method which estimates the normalized point-source sensitivity (PSSN) of a segmented telescope when only information from a single segment surface is known. The estimation principle is based on a statistical approach with an assumption that all segment surfaces have the same power spectral density (PSD) as the given segment surface. As presented in this paper, the PSSN based on this statistical approach represents a worst-case scenario among statistical random realizations of telescopes when all segment surfaces have the same PSD. Therefore, this method, which we call the vendor table, is expected to be useful for individual segment specification such as the segment polishing specification. The specification based on the vendor table can be directly related to a science metric such as PSSN and provides the mirror vendors significant flexibility by specifying a single overall PSSN value for them to meet. We build a vendor table for the Thirty Meter Telescope (TMT) and test it using multiple mirror samples from various mirror vendors to prove its practical utility. Accordingly, TMT has a plan to adopt this vendor table for its M1 segment final mirror polishing requirement.
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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 | 0.000 |
| 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 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".