The radio loudness of SDSS quasars from the LOFAR Two-metre Sky Survey: ubiquitous jet activity and constraints on star formation
Bibliographic record
Abstract
ABSTRACT We examine the distribution of radio emission from ∼42 000 quasars from the Sloan Digital Sky Survey, as measured in the LOFAR Two-metre Sky Survey (LoTSS). We present a model of the radio luminosity distribution of the quasars that assumes that every quasar displays a superposition of two sources of radio emission: active galactic nuclei (jets) and star formation. Our two-component model provides an excellent match to the observed radio flux density distributions across a wide range of redshifts and quasar optical luminosities; this suggests that the jet-launching mechanism operates in all quasars but with different powering efficiency. The wide distribution of jet powers allows for a smooth transition between the ‘radio-quiet’ and ‘radio-loud’ quasar regimes, without need for any explicit bimodality. The best-fitting model parameters indicate that the star formation rate of quasar host galaxies correlates strongly with quasar luminosity and also increases with redshift at least out to z ∼ 2. For a model where star formation rate scales as $L_{\rm bol}^{\alpha } (1+z)^{\beta }$, we find α = 0.47 ± 0.01 and β = 1.61 ± 0.05, in agreement with far-infrared studies. Quasars contribute ≈0.15 per cent of the cosmic star formation rate density at z = 0.5, rising to 0.4 per cent by z ∼ 2. The typical radio jet power is seen to increase with both increasing optical luminosity and black hole mass independently, but does not vary with redshift, suggesting intrinsic properties govern the production of the radio jets. We discuss the implications of these results for the triggering of quasar activity and the launching of jets.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".