Microshutter array system for James Webb Space Telescope
Bibliographic record
Abstract
We have developed microshutter array systems at NASA Goddard Space Flight Center for use as multi-object aperture arrays for a Near-Infrared Spectrometer (NIRSpec) instrument. The instrument will be carried on the James Webb Space Telescope (JWST), the next generation of space telescope, after the Hubble Space Telescope retires. The microshutter arrays (MSAs) are designed for the selective transmission of light from objected galaxies in space with high efficiency and high contrast. Arrays are close-packed silicon nitride membranes with a pixel size close to 100x200 μm. Individual shutters are patterned with a torsion flexure permitting shutters to open 90 degrees with minimized stress concentration. In order to enhance optical contrast, light shields are made on each shutter to prevent light leak. Shutters are actuated magnetically, latched and addressed electrostatically. The shutter arrays are fabricated using MEMS bulk-micromachining and packaged utilizing a novel single-sided indium flip-chip bonding technology. The MSA flight system consists of a mosaic of 2 x 2 format of four fully addressable 365 x 171 arrays. The system will be placed in the JWST optical path at the focal plane of NIRSpec detectors. MSAs that we fabricated passed a series of qualification tests for flight capabilities. We are in the process of making final flight-qualified MSA systems for the JWST mission.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.099 | 0.045 |
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".