The Sloan Digital Sky Survey Reverberation Mapping Project: Initial C iv Lag Results from Four Years of Data
Bibliographic record
Abstract
Abstract We present reverberation-mapping (RM) lags and black hole mass measurements using the C iv λ 1549 broad emission line from a sample of 348 quasars monitored as a part of the Sloan Digital Sky Survey RM Project. Our data span four years of spectroscopic and photometric monitoring for a total baseline of 1300 days, allowing us to measure lags up to ∼750 days in the observed frame (this corresponds to a rest-frame lag of ∼300 days in a quasar at z = 1.5 and ∼190 days at z = 3). We report significant time delays between the continuum and the C iv λ 1549 emission line in 48 quasars, with an estimated false-positive detection rate of 10%. Our analysis of marginal lag measurements indicates that there are on the order of ∼100 additional lags that should be recoverable by adding more years of data from the program. We use our measurements to calculate black hole masses and fit an updated C iv radius–luminosity relationship. Our results significantly increase the sample of quasars with C iv RM results, with the quasars spanning two orders of magnitude in luminosity toward the high-luminosity end of the C iv radius–luminosity relation. In addition, these quasars are located at some of the highest redshifts ( z ≈ 1.4–2.8) of quasars with black hole masses measured with RM. This work constitutes the first large sample of C iv RM measurements in more than a dozen quasars, demonstrating the utility of multiobject RM campaigns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".