The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results
Bibliographic record
Abstract
Abstract We present the final data from the Sloan Digital Sky Survey (SDSS) Reverberation Mapping (RM) project, a precursor to the SDSS-V Black Hole Mapper RM program. This data set includes 11 yr photometric and 7 yr spectroscopic light curves for 849 broad-line quasars over a redshift range of 0.1 < z < 4.5 and a luminosity range of L bol = 10 44−47.5 erg s −1 , along with spectral and variability measurements. We report 23, 81, 125, and 110 RM lags (relative to optical continuum variability) for broad H α , H β , Mg ii , and C iv using the SDSS-RM sample, spanning much of the luminosity and redshift ranges of the sample. Using 30 low-redshift RM active galactic nuclei with dynamical-modeling black hole masses, we derive a new estimate of the average virial factor of log f = 0.62 ± 0.07 for the line dispersion measured from the rms spectrum. The intrinsic scatter of individual virial factors is 0.31 ± 0.07 dex, indicating a factor of 2 systematic uncertainty in RM black hole masses. Our lag measurements reveal significant R – L relations for H β and Mg ii at high redshift, consistent with the latest measurements based on heterogeneous samples. While we are unable to robustly constrain the slope of the R – L relation for C iv given the limited dynamic range in luminosity, we found substantially larger scatter in C iv lags at fixed L 1350 . Using the SDSS-RM lag sample, we derive improved single-epoch (SE) mass recipes for H β , Mg ii , and C iv , which are consistent with their respective RM masses as well as between the SE recipes from two different lines, over the luminosity range probed by our sample. The new H β and Mg ii recipes are approximately unbiased estimators at given RM masses, but there are systematic biases in the C iv recipe. The intrinsic scatter of SE masses around RM masses is ∼0.45 dex for H β and Mg ii , increasing to ∼0.58 dex for C iv .
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.017 |
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".