Approximation of normalized point source sensitivity using power spectral density and slopes of wavefront aberration
Bibliographic record
Abstract
We have investigated two approximation methods for estimating the normalized point source sensitivity (PSSN), which is a recently developed optical performance metric for telescopes. One is an approximation based on the power spectral density (PSD) of the wavefront error. The other is the root-square-sum of the wavefront slope. We call these approximations β approximation and SlopeRMS approximation, respectively. Our analysis shows that for the Thirty Meter Telescope (TMT), the uncertainty of the β approximation is less than 1×10(-3) if the PSSN is better than 0.95, assuming the input PSD estimation is accurate. In addition, we find that the SlopeRMS approximation is a simple method for estimating the worst-case PSSN value in the specific situation when the PSSN is dominated by low-frequency aberrations. Therefore, the SlopeRMS approximation is expected to be useful for specifying a mirror surface for mirror vendors. Accordingly, TMT has a plan to adopt the SlopeRMS approximation for its M2 and M3 polishing specification.
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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".