The Sloan Digital Sky Survey Reverberation Mapping Project: Mg ii Lag Results from Four Years of Monitoring
Bibliographic record
Abstract
Abstract We present reverberation mapping results for the Mg ii λ2800 Å broad emission line in a sample of 193 quasars at 0.35 < z < 1.7 with photometric and spectroscopic monitoring observations from the Sloan Digital Sky Survey Reverberation Mapping project during 2014–2017. We find significant time lags between the Mg ii and continuum lightcurves for 57 quasars, and define a “gold sample” of 24 quasars with the most reliable lag measurements. We estimate false-positive rates for each lag that range from 1% to 24%, with an average false-positive rate of 11% for the full sample and 8% for the gold sample. There are an additional ∼40 quasars with marginal Mg ii lag detections, which may yield reliable lags after additional years of monitoring. The Mg ii lags follow a radius–luminosity relation with a best-fit slope that is consistent with α = 0.5 , but with an intrinsic scatter of 0.36 dex that is significantly larger than found for the Hβ radius–luminosity relation. For targets with SDSS-RM lag measurements of other emission lines, we find that our Mg ii lags are similar to the Hβ lags and ∼2–3 times larger than the C iv lags. This work significantly increases the number of Mg ii broad-line lags and provides additional reverberation-mapped black hole masses, filling the redshift gap at the peak of supermassive black hole growth between the Hβ and C iv emission lines in optical spectroscopy.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".