The Sloan Digital Sky Survey Reverberation Mapping Project: Hα and Hβ Reverberation Measurements from First-year Spectroscopy and Photometry
Bibliographic record
Abstract
Abstract We present reverberation mapping results from the first year of combined spectroscopic and photometric observations of the Sloan Digital Sky Survey Reverberation Mapping Project. We successfully recover reverberation time delays between the g+i band emission and the broad Hβ emission line for a total of 44 quasars, and for the broad Hα emission line in 18 quasars. Time delays are computed using the JAVELIN and CREAM software and the traditional interpolated cross-correlation function (ICCF): using well-defined criteria, we report measurements of 32 Hβ and 13 Hα lags with JAVELIN, 42 Hβ and 17 Hα lags with CREAM, and 16 Hβ and eight Hα lags with the ICCF. Lag values are generally consistent among the three methods, though we typically measure smaller uncertainties with JAVELIN and CREAM than with the ICCF, given the more physically motivated light curve interpolation and more robust statistical modeling of the former two methods. The median redshift of our Hβ-detected sample of quasars is 0.53, significantly higher than that of the previous reverberation mapping sample. We find that in most objects, the time delay of the Hα emission is consistent with or slightly longer than that of Hβ. We measure black hole masses using our measured time delays and line widths for these quasars. These black hole mass measurements are mostly consistent with expectations based on the local – relationship, and are also consistent with single-epoch black hole mass measurements. This work increases the current sample size of reverberation-mapped active galaxies by about two-thirds and represents the first large sample of reverberation mapping observations beyond the local universe (z < 0.3).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".