The JWST/NIRSpec instrument performance simulator
Bibliographic record
Abstract
The future James Webb space telescope (JWST), developed jointly by the American, European and Canadian space agencies (NASA, ESA and CSA), is scheduled for launch in 2013. Among its instrument suite, the spectrograph NIRSpec will provide astronomers with multi-object, integral-field and classical slit spectrographic capabilities in the near-infrared (0.6-5.0 μm). NIRSpec is being built by EADS Astrium for ESA and it was quickly realized that given the complexity of the instrument, it was necessary to develop dedicated software for the modeling of its performances. In this context, the Centre de Recherche Astrophysique de Lyon (CRAL) is responsible of the development of the so-called NIRSpec instrument performance simulator (IPS) that will serve as a basis for early performance verification purposes; provide inputs and support for the verification and calibration campaigns, as well as for the development of the instrument calibration, target acquisition and data reduction procedures. In this paper, we present the IPS software itself, emphasizing its capability to generate simulated NIRSpec detector exposures for the various modes of the instrument (multi-object, integral field unit, fixed slits) and for a large variety of situations (test, calibration, scientific observations...). We will also show simulations results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".