Active Galactic Nuclei Selection and Demographics: A New Age with JWST/MIRI
Bibliographic record
Abstract
Abstract Understanding the coevolution of supermassive black holes and their host systems requires a comprehensive census of active galactic nuclei (AGNs) behavior across a wide range of redshift, luminosity, obscuration level, and galaxy properties. We report significant progress with JWST toward this goal from the Systematic Mid-infrared Instrument Legacy Extragalactic Survey (SMILES). Based on comprehensive spectral energy distribution (SED) analysis of 3273 MIRI-detected sources, we identify 217 AGN candidates over a survey area of ∼34 arcmin 2 , including a primary sample of 111 AGNs in normal massive galaxies ( M * > 10 9.5 M ☉ ) at z ∼ 0–4, an extended sample of 86 AGN candidates in low-mass galaxies ( M * < 10 9.5 M ☉ ), and a high- z sample of 20 AGN candidates at z ∼ 4–8.4. Notably, about 80% of our MIRI-selected AGN candidates are new discoveries despite the extensive pre-JWST AGN searches. Even among the massive galaxies where the previous AGN search is believed to be thorough, 34% of the MIRI AGN identifications are new, highlighting the impact of obscuration on previous selections. By combining our results with the efforts at other wavelengths, we build the most complete AGN sample to date and examine the relative performance of different selection techniques. We find the obscured AGN fraction increases from L AGN,bol ∼ 10 10 L ⊙ to 10 11 L ⊙ and then drops toward higher luminosity. Additionally, the obscured AGN fraction gradually increases from z ∼ 0 to z ∼ 4 with most high- z AGNs obscured. We discuss how AGN obscuration, intrinsic SED variations, galaxy contamination, survey depth, and selection techniques complicate the construction of a complete AGN sample.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".