The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope. I. Instrument Overview and In-flight Performance
Bibliographic record
Abstract
Abstract The Near-Infrared Imager and Slitless Spectrograph (NIRISS) is the science module of the Canadian-built Fine Guidance Sensor onboard the James Webb Space Telescope (JWST). NIRISS has four observing modes: (1) broadband imaging featuring seven of the eight NIRCam broadband filters, (2) wide-field slitless spectroscopy at a resolving power of ∼150 between 0.8 and 2.2 μm, (3) single-object cross-dispersed slitless spectroscopy (SOSS) enabling simultaneous wavelength coverage between 0.6 and 2.8 μm at R ∼ 700, a mode optimized for exoplanet spectroscopy of relatively bright (J < 6.3) stars and (4) aperture masking interferometry (AMI) between 2.8 and 4.8 μm enabling high-contrast (∼10−3 − 10−4) imaging at angular separations between 70 and 400 mas for relatively bright (M < 8) sources. This paper presents an overview of the NIRISS instrument, its design, its scientific capabilities, and a summary of in-flight performance. NIRISS shows significantly better response shortward of ∼2.5 μm resulting in 10%–40% sensitivity improvement for broadband and low-resolution spectroscopy compared to pre-flight predictions. Two time-series observations performed during instrument commissioning in the SOSS mode yield very stable spectro-photometry performance within ∼10% of the expected noise. The first space-based companion detection of the tight binary star AB Dor AC through AMI was demonstrated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".