Efficiency Measurements and Installation of a New Grating for the OSIRIS Spectrograph at Keck Observatory
Bibliographic record
Abstract
OSIRIS is a near-infrared integral field spectrograph operating behind the adaptive optics system at W. M. Keck Observatory. While OSIRIS has been a scientifically productive instrument to date, its sensitivity has been limited by a grating efficiency that is less than half of what was expected. The spatially averaged efficiency of the old grating, weighted by error, is measured to be 39.5% ± 0.8% at λ = 1.310 μm, with a large field-dependent variation of 11.7% due to efficiency variation across the grating surface. Working with a new vendor, we developed a more efficient and uniform grating with a weighted average efficiency at λ = 1.310 μm of 78.0% ± 1.6%, with field variation of only 2.2%. This is close to double the average efficiency and 5 times less variation across the field. The new grating was installed in 2012 December, and on-sky OSIRIS throughput shows an average factor of 1.83 improvement in sensitivity between 1 and 2.4 μm. We present the development history, testing, and implementation of this new near-infrared grating for OSIRIS and report on the comparison with the predecessors. The higher sensitivities are already having a large impact on scientific studies with OSIRIS.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".