EPOCHS VII: discovery of high-redshift (6.5 &lt; <i>z</i> &lt; 12) AGN candidates in JWST ERO and PEARLS data
Bibliographic record
Abstract
ABSTRACT We present an analysis of a sample of robust high-redshift galaxies selected from the ‘blank’ fields of the Prime Extragalactic Areas for Reionization Science (PEARLS) survey and Early Release Observations (ERO) data from JWST with the aim of selecting candidate high-redshift active galactic nuclei (AGN). Sources were identified from this parent sample using a threefold selection procedure, which includes spectral energy distribution (SED) fitting to identify sources that are best fitted by AGN SED templates, a further selection based on the relative performance of AGN and non-AGN models, and finally morphological fitting to identify compact sources of emission, resulting in a purity-oriented procedure. Using this procedure, we identify a sample of nine AGN candidates at 6.5 < z < 12, from which we constrain their physical properties as well as measure a lower bound on the AGN fraction in this redshift range of 5 ± 1 per cent. As this is an extreme lower limit due to our focus on purity and our SEDs being calibrated for unobscured Type 1 AGN, this demonstrates that AGN are perhaps quite common at this early epoch. The rest-frame UV colours of our candidate objects suggest that these systems are potentially candidate obese black hole galaxies (OBGs). We also investigate Chandra and VLA maps of these areas from which we calculate detection limits. Of note is a z = 11.9 candidate source exhibiting an abrupt morphological shift in the reddest band as compared to bluer bands, indicating a potential merger or an unusually strong outflow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".