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 μ m, (3) single-object cross-dispersed slitless spectroscopy (SOSS) enabling simultaneous wavelength coverage between 0.6 and 2.8 μ m 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 μ m 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 μ m 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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".