Making good use of JWST's coronagraphs: tools and strategies from a user's perspective
Bibliographic record
Abstract
The James Webb Space Telescope (JWST) and its suite of instruments, modes and high contrast capabilities will enable imaging and characterization of faint and dusty astrophysical sources<sup>1-3</sup> (exoplanets, proto-planetary and debris disks, dust shells, etc.) in the vicinity of hosts (stars of all sorts, active galactic nuclei, etc.) with an unprecedented combination of sensitivity and angular resolution at wavelengths beyond 2 μm. Two of its four instruments, NIRCam<sup>4, 5</sup> and MIRI,<sup>6</sup> feature coronagraphs<sup>7, 8</sup> for wavelengths from 2 to 23 μm. JWST will stretch the current parameter space (contrast at a given separation) towards the infrared with respect to the Hubble Space Telescope (HST) and in sensitivity with respect to what is currently achievable from the ground with the best adaptive optics (AO) facilities. The Coronagraphs Working Group at the Space Telescope Science Institute (STScI) along with the Instruments Teams and internal/external partners coordinates efforts to provide the community with the best possible preparation tools, documentation, pipelines, etc. Here we give an update on user support and operational aspects related to coronagraphy. We aim at demonstrating an end to end observing strategy and data management chain for a few science use cases involving coronagraphs. This includes the choice of instrument modes as well as the observing and point-spread function (PSF) subtraction strategies (e.g. visibility, reference stars selection tools, small grid dithers), the design of the proposal with the Exposure Time Calculator (ETC), and the Astronomer's Proposal Tool (APT), the generation of realistic simulated data at small working angles and the generation of high level, science-grade data products enabling calibration and state of the art data-processing.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".