Analysis of normalized point source sensitivity as a performance metric for large telescopes
Bibliographic record
Abstract
We investigate a new metric, the normalized point source sensitivity (PSSN), for characterizing the seeing-limited performance of large telescopes. As the PSSN metric is directly related to the photometric error of background limited observations, it represents the efficiency loss in telescope observing time. The PSSN metric properly accounts for the optical consequences of wave front spatial frequency distributions due to different error sources, which differentiates from traditional metrics such as the 80% encircled energy diameter and the central intensity ratio. We analytically show that multiplication of individual PSSN values due to individual errors is a good approximation for the total PSSN when various errors are considered simultaneously. We also numerically confirm this feature for Zernike aberrations as well as for the numerous error sources considered in the error budget of the Thirty Meter Telescope (TMT) using a ray optics simulator. Additionally, we discuss other pertinent features of the PSSN, including its relations to Zernike aberration, RMS wave front error, and central intensity ratio.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".