Analysis of normalized point source sensitivity as a performance metric for large telescopes
Why this work is in the frame
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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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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 | 0.001 |
| 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 it