The Sloan Digital Sky Survey Reverberation Mapping Project: The Hβ Radius–Luminosity Relation
Bibliographic record
Abstract
Abstract Results from a few decades of reverberation mapping (RM) studies have revealed a correlation between the radius of the broad-line emitting region (BLR) and the continuum luminosity of active galactic nuclei. This “radius–luminosity” relation enables survey-scale black hole mass estimates across cosmic time, using relatively inexpensive single-epoch spectroscopy, rather than intensive RM time monitoring. However, recent results from newer RM campaigns challenge this widely used paradigm, reporting quasar BLR sizes that differ significantly from the previously established radius–luminosity relation. Using simulations of the radius–luminosity relation with the observational parameters of the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project, we find that this difference is not likely due to observational biases. Instead, it appears that previous RM samples were biased to a subset of quasar properties, and the broader parameter space occupied by the SDSS-RM quasar sample has a genuinely wider range of BLR sizes. We examine the correlation between the deviations from the radius–luminosity relation and several quasar parameters; the most significant correlations indicate that the deviations depend on the UV/optical spectral energy distribution and the relative amount of ionizing radiation. Our results indicate that single-epoch black hole mass estimates that do not account for the diversity of quasars in the radius–luminosity relation could be overestimated by an average of ∼0.3 dex.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".