HerMES: disentangling active galactic nuclei and star formation in the radio source population
Bibliographic record
Abstract
We separate the extragalactic radio source population above ∼50 μJy into active galactic nuclei (AGN) and star-forming sources. The primary method of our approach is to fit the infrared spectral energy distributions (SEDs), constructed using Spitzer/IRAC (Infrared Array Camera) and Multiband Imaging Photometer for Spitzer (MIPS) and Herschel/SPIRE photometry, of 380 radio sources in the Extended Chandra Deep Field-South. From the fitted SEDs, we determine the relative AGN and star-forming contributions to their infrared emission. With the inclusion of other AGN diagnostics such as X-ray luminosity, Spitzer/IRAC colours, radio spectral index and the ratio of star-forming total infrared flux to k-corrected 1.4 GHz flux density, qIR, we determine whether the radio emission in these sources is powered by star formation or by an AGN. The majority of these radio sources (60 per cent) show the signature of an AGN at some wavelength. Of the sources with AGN signatures, 58 per cent are hybrid systems for which the radio emission is being powered by star formation. This implies that radio sources which have likely been selected on their star formation have a high AGN fraction. Below a 1.4 GHz flux density of 1 mJy, along with finding a strong contribution to the source counts from pure star-forming sources, we find that hybrid sources constitute 20–65 per cent of the sources. This result suggests that hybrid sources have a significant contribution, along with sources that do not host a detectable AGN, to the observed flattening of the source counts at ∼1 mJy for the extragalactic radio source population.
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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 | 0.001 | 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) | 0.004 | 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".