Performance of near-infrared high-contrast imaging methods with JWST from commissioning
Bibliographic record
Abstract
The James Webb Space Telescope (JWST) will revolutionize the field of high-contrast imaging and enable both the direct detection of Saturn-mass planets and the characterization of substellar companions in the mid-infrared. While JWST will feature unprecedented sensitivity, angular resolution will be the key factor when competing with ground-based telescopes. Here, we aim to characterize the performance of several extreme angular resolution imaging techniques available with JWST in the 3–5 µm regime based on data taken during the instrument commissioning. Firstly, we introduce custom tools to simulate, reduce, and analyze JWST NIRCam and MIRI coronagraphy data and use these tools to extract companion detection limits from on-sky NIRCam round and bar mask coronagraphy observations. Secondly, we present on-sky JWST NIRISS aperture masking interferometry (AMI) and kernel phase imaging (KPI) observations from which we extract companion detection limits using the publicly available fouriever tool. Scaled to a total integration time of one hour and a target of the brightness of AB Dor (W1 ≈ 4.4 mag, W2 ≈ 3.9 mag), we find that NIRISS AMI and KPI reach contrasts of ∼ 7–8 mag at ∼ 70 mas and ∼ 9 mag at ∼ 200 mas. Beyond ∼ 250 mas, NIRCam coronagraphy reaches deeper contrasts of ∼ 13 mag at ∼ 500 mas and ∼ 15 mag at ∼ 2 arcsec. While the bar mask performs ∼ 1 mag better than the round mask at small angular separations ≲ 0.75 arcsec, it is the other way around at large angular separations ≳ 1.5 arcsec. Moreover, the round mask gives access to the full 360 deg field-of-view which is beneficial for the search of new companions. We conclude that already during the instrument commissioning, JWST high-contrast imaging in the L- and M-bands performs close to its predicted limits and is a factor of ∼ 10 times better at large separations than the best ground-based instruments operating at similar wavelengths despite its < 2 times smaller collecting area.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".