ACTIVE GALACTIC NUCLEUS AND QUASAR SCIENCE WITH APERTURE MASKING INTERFEROMETRY ON THE<i>JAMES WEBB SPACE TELESCOPE</i>
Bibliographic record
Abstract
Due to feedback from accretion onto supermassive black holes (SMBHs), active galactic nuclei (AGNs) are believed to play a key role in ΛCDM cosmology and galaxy formation. However, AGNs extreme luminosities and the small angular size of their accretion flows create a challenging imaging problem. We show that the James Webb Space Telescope 's Near Infrared Imager and Slitless Spectrograph ( JWST -NIRISS) Aperture Masking Interferometry (AMI) mode will enable true imaging (i.e., without any requirement of prior assumptions on source geometry) at ∼65 mas angular resolution at the centers of AGNs. This is advantageous for studying complex extended accretion flows around SMBHs and in other areas of angular-resolution-limited astrophysics. By simulating data sequences incorporating expected sources of noise, we demonstrate that JWST -NIRISS AMI mode can map extended structure at a pixel-to-pixel contrast of ∼10 −2 around an L = 7.5 point source, using short exposure times (minutes). Such images will test models of AGN feedback, fueling, and structure (complementary with ALMA observations), and are not currently supported by any ground-based IR interferometer or telescope. Binary point source contrast with NIRISS is ∼10 −4 (for observing binary nuclei in merging galaxies), significantly better than current ground-based optical or IR interferometry. JWST -NIRISS's seven-hole non-redundant mask has a throughput of 15%, and utilizes NIRISS's F277W (2.77 μm), F380M (3.8 μm), F430M (4.3 μm), and F480M (4.8 μm) filters. NIRISS's square pixels are 65 mas per side, with a field of view ∼2' × 2'. We also extrapolate our results to AGN science enabled by non-redundant masking on future 2.4 m and 16 m space telescopes working at long-UV to near-IR wavelengths.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".