Space qualification of an antireflection coating on the surface of a ruled grating prism: increasing the throughput of the single-object slitless spectroscopy mode of NIRISS onboard JWST
Bibliographic record
Abstract
Grating prisms (grism) designed for near-infrared spectroscopy typically make use of high-refractive index materials such as zinc selenide (ZnSe), at the expense of large Fresnel losses ( 18%). Part of the loss can be recovered by using anti-reflection (AR) coatings. The technique is however considered risky when applied on the ruled surface of a grating, especially for a space application at cryogenic temperature. Such a grism, made of ZnSe and machined at Lawrence Livermore National Laboratory (LLNL) is mounted in the Near-Infrared Slitless Spectrograph (NIRISS) onboard the James Webb Space Telescope (JWST). Its Single Object Slitless Spectrograph (SOSS) observing mode uses the ZnSe grism and a cross-dispersing prism to produce R=700 spectra in orders 1 and 2 to cover the 0.6 to 2.5 microns spectral domain. The ZnSe grism is blazed at 1.23 microns, has a density of 54 lines/mm and its triangular grooves have a depth of 700 nm, a base of 18 microns, with facets angled at 1.9 degrees. Here, an AR coating produced by Thin Film Lab (TFL) and deposited on the ruled surface of a ZnSe grism sample was space qualified. Atomic force microscopy (AFM) showed no groove profile change pre/post coating despite the large relative thickness of the AR coating to that of the groove depth ( 35%). Also, the wavefront error map remained almost unchanged at lambda/8 (peak-to-valley at 632 nm) and survived unscathed through a series of three cryogenic cycles to 20 K. Finally, the transmission gain across our operating spectral range was almost as high as that for a unruled surface covered with the same AR coating (10-15%).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".