Commissioning and calibration of the JWST Aperture Masking Interferometry mode
Bibliographic record
Abstract
The multi-national James Webb Space Telescope (JWST) enables several new technologies, one of which is the first space-based infrared interferometer, the Aperture Masking Interferometry (AMI) mode of the Near Infrared Imager and Slitless Spectrograph (NIRISS). AMI is a niche but powerful tool for high resolution imaging of a variety of moderate- to high-contrast astronomical sources. The non-redundant mask (NRM) in the entrance pupil enables detection of structure below the classical Rayleigh diffraction limit, well inside the inner working angle of JWST’s coronagraphs. This explores a parameter space largely inaccessible to existing ground- and other space-based observatories. Early science observations leveraged the capabilities of this unique mode to observe dusty Wolf-Rayet binaries, spatially resolved solar system objects, massive exoplanet systems, and protoplanetary disks. The high quality of this space-based data demonstrated the need for improved analysis methods. We describe approaches to extracting interferometric observables, as well as pre- and post-extraction data cleaning routines we made available to the user community. We also discuss insights and unique challenges that were revealed during the commissioning, early calibration, and first science cycles of this promising observing mode: mitigation strategies for instrumental effects, lessons learned for optimizing observation configuration, and plans for ongoing calibration efforts. Knowledge gained from commissioning and calibration data – which are always non-proprietary – provide valuable insight into the capabilities and limitations of this mode, highlight areas that need improvement, and lay the groundwork for furthering JWST’s scientific objectives.
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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 | 0.000 | 0.000 |
| 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".