Connecting MeerKAT Radio Continuum Properties to GAMA Optical Emission-line and WISE Mid-infrared Activity
Bibliographic record
Abstract
Abstract The identification of active galactic nuclei (AGNs) in large surveys has been hampered by seemingly discordant classifications arising from differing diagnostic methods, usually tracing distinct processes specific to a particular wavelength regime. However, as shown in Yao et al., the combination of optical emission-line measurements and mid-infrared photometry can be used to optimize the discrimination capability between AGN and star formation activity. In this paper we test our new classification scheme by combining the existing GAMA-WISE data with high-quality MeerKAT radio continuum data covering 8 deg2 of the GAMA G23 region. Using this sample of 1841 galaxies (z < 0.25), we investigate the total infrared (derived from 12 μm) to radio luminosity ratio, q (TIR), and its relationship to optical–infrared AGN and star-forming (SF) classifications. We find that while q (TIR) is efficient at detecting AGN activity in massive galaxies generally appearing quiescent in the infrared, it becomes less reliable for cases where the emission from star formation in the host galaxy is dominant. However, we find that the q (TIR) can identify up to 70% more AGNs not discernible at optical and/or infrared wavelengths. The median q (TIR) of our SF sample is 2.57 ± 0.23, consistent with previous local universe estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".