The Sloan Digital Sky Survey Reverberation Mapping Project: Accretion Disk Sizes from Continuum Lags
Bibliographic record
Abstract
Abstract We present accretion disk structure measurements from continuum lags in the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project. Lags are measured using the JAVELIN software from the first-year SDSS-RM g and i photometry, resulting in well-defined lags for 95 quasars, 33 of which have lag S/N > 2 σ . We also estimate lags using the ICCF software and find consistent results, though with larger uncertainties. Accretion disk structure is fit using a Markov chain Monte Carlo approach, parameterizing the measured continuum lags as a function of disk size normalization, wavelength, black hole mass, and luminosity. In contrast with previous observations, our best-fit disk sizes and color profiles are consistent (within 1.5 σ ) with the Shakura & Sunyaev analytic solution. We also find that more massive quasars have larger accretion disks, similarly consistent with the analytic accretion disk model. The data are inconclusive on a correlation between disk size and continuum luminosity, with results that are consistent with both no correlation and the Shakura & Sunyaev expectation. The continuum lag fits have a large excess dispersion, indicating that our measured lag errors are underestimated and/or our best-fit model may be missing the effects of orientation, spin, and/or radiative efficiency. We demonstrate that fitting disk parameters using only the highest-S/N lag measurements biases best-fit disk sizes to be larger than the disk sizes recovered using a Bayesian approach on the full sample of well-defined lags.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".