Placing LOFAR-detected quasars in C <scp>iv</scp> emission space: implications for winds, jets and star formation
Bibliographic record
Abstract
ABSTRACT We present an investigation of the low-frequency radio and ultraviolet properties of a sample of ≃10 500 quasars from the Sloan Digital Sky Survey Data Release 14, observed as part of the first data release of the Low-Frequency-Array Two-metre Sky Survey. The quasars have redshifts 1.5 < z < 3.5 and luminosities $44.6 \lt \log _{10}\left(L_{\text{bol}}/\rm{erg\,s}^{-1}\right) \lt 47.2$. We employ ultraviolet spectral reconstructions based on an independent component analysis to parametrize the C iv λ1549-emission line that is used to infer the strength of accretion disc winds, and the He ii λ1640 line, an indicator of the soft X-ray flux. We find that radio-detected quasars are found in the same region of C iv blueshift versus equivalent-width space as radio-undetected quasars, but that the loudest, most luminous and largest radio sources exist preferentially at low C iv blueshifts. Additionally, the radio-detection fraction increases with blueshift whereas the radio-loud fraction decreases. In the radio-quiet population, we observe a range of He ii equivalent widths as well as a Baldwin effect with bolometric luminosity, whilst the radio-loud population has mostly strong He ii, consistent with a stronger soft X-ray flux. The presence of strong He ii is a necessary but not sufficient condition to detect radio-loud emission suggesting some degree of stochasticity in jet formation. Using energetic arguments and Monte Carlo simulations, we explore the plausibility of winds, compact jets, and star formation as sources of the radio quiet emission, ruling out none. The existence of quasars with similar ultraviolet properties but differing radio properties suggests, perhaps, that the radio and ultraviolet emission is tracing activity occurring on different time-scales.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".