The European optical contribution to the James Webb Space Telescope
Bibliographic record
Abstract
Abstract The James Webb Space Telescope (JWST) is frequently referred to as the follow-on mission to the Hubble Space Telescope (HST). The ‘Webb’ will be the biggest space telescope ever built and is expected to enable astounding new science. The observatory comprises a 6.5-m-diameter telescope with a segmented primary mirror and four high-performance optical science instruments. The JWST has mostly been optimized to work in the near- (0.6–5.0 μm) and mid-infrared (5.0–29 μm) wavelength regions. The project is a strong international partnership led by the National Aeronautics and Space Administration (NASA) with contributions from the European Space Agency (ESA) and the Canadian Space Agency (CSA). The observatory is currently scheduled for launch in early 2021 from Kourou, French Guyana, by an ESA-provided Ariane 5 rocket. This paper will focus on the European optical contribution to the mission, which mainly consists of two highly advanced optical science instruments: The multi-object near-infrared spectrograph (NIRSpec) and the mid-infrared instrument (MIRI). The opto-mechanical design considerations and the realization of both instruments will be described, and we will conclude with a short JWST project status report and future outlook.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".