Bibliographic record
Abstract
During an AO observation the Lyot stop of an infrared instrument fed by the AO system must remain aligned with the image of the telescope pupil.Uncertainties and drifts of the pupil image position must be accommodated by under-sizing the Lyot stop, which, especially for AO corrected observations, reduces very significantly the science productivity of the instrument.In the case of IRIS, the first client instrument of NFIRAOS on the TMT, there is a requirement to limit the under-sizing of the IRIS Lyot stop to 1% of the pupil diameter, which therefore means to tightly stabilize the image of the telescope pupil.In this paper, we show how this can be accomplished by finely aligning the IRIS Lyot stop to NFIRAOS during calibration (sighting actuators pokes on the NFIRAOS high-altitude DM with the IRIS pupil viewing camera), and, during observation, by processing the images obtained by the NFIRAOS Truth WFS to detect drifts and feeding this drift information to the telescope control system so it can repoint the beam fed to NFIRAOS.We show that this measurement of the pupil position with the Truth WFS can be accomplished in nominal observing conditions with an RMS error of less than 0.02% of the pupil diameter in bright time, and less than 0.03% of the pupil diameter in dark time.We include in this evaluation noise sources on the Truth WFS, as well as uncertainties on the reflectivity of the primary mirror segments due to their recoating schedule.This work directly feeds into the work presented in reference [1], which describes the full tolerance stack-up required to reach the 1% under-sizing of the Lyot stop.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".