A novel Bayesian approach for decomposing the radio emission of quasars – II. Link between quasar radio emission and black hole mass
Bibliographic record
Abstract
ABSTRACT Whether the mass of supermassive black hole ($M_\mathrm{BH}$) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of $M_\mathrm{BH}$ in the evolution of quasars is pivotal to unveiling the mechanism of active galactic nucleus (AGN) feedback. In this work, based on a two-component Bayesian model, we examine how $M_\mathrm{BH}$ affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between $M_\mathrm{BH}$ and star formation rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most $M_\mathrm{BH}$ values; however, quasars with the top 20 per cent most massive supermassive black holes (SMBHs) are two to three times more likely to host strong radio jets than those with lower mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of $M_\mathrm{BH}$ in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20 per cent most massive SMBHs affects only the $\sim 5~{{\rm per\ cent}}$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".