Exploring the radio loudness of SDSS quasars with spectral stacking
Bibliographic record
Abstract
ABSTRACT We use new 144 MHz observations over 5634 deg2 from the LOFAR (Low Frequency Array) Two-metre Sky Survey (LoTSS) to compile the largest sample of uniformly selected, spectroscopically confirmed quasars from the 14th data release of the Sloan Digital Sky Survey (SDSS-DR14). Using the classical definition of radio loudness, R = log (L1.4GHz/Li), we identify 3697 radio-loud (RL) and 111 132 radio-quiet (RQ) sources at 0.6 < z < 3.4. To study their properties, we develop a new rest-frame spectral stacking algorithm, designed with forthcoming massively multiplexed spectroscopic surveys in mind, and use it to create high signal-to-noise composite spectra of each class, matched in redshift and absolute i-band magnitude. We show that RL quasars have redder continuum and enhanced [O ii] emission than their RQ counterparts. These results persist when additionally matching in black hole mass, suggesting that this parameter is not the defining factor in making a quasi-stellar object (QSO) RL. We find that these features are not gradually varying as a function of radio loudness, but are maintained even when probing deeper into the RQ population, indicating that a clear-cut division in radio loudness is not apparent. Upon examining the star formation rates (SFRs) inferred from the [O ii] emission line, with the contribution from active galactic nucleus removed using the [Ne v] line, we find that RL quasars have a significant excess of star formation relative to RQ quasars out to z = 1.9 at least. Given our findings, we suggest that RL sources either preferably reside in gas-rich systems with rapidly spinning black holes, or represent an earlier obscured phase of QSO evolution.
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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 | 0.004 | 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) | 0.001 | 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".