The Sloan Digital Sky Survey Reverberation Mapping Project: UV-Optical Accretion Disk Measurements with Hubble Space Telescope
Bibliographic record
Abstract
We present accretion-disk structure measurements from UV-optical reverberation mapping observations of a sample of eight quasars at 0.24<z<0.85. Ultraviolet photometry comes from two cycles of Hubble Space Telescope monitoring, accompanied by multi-band optical monitoring by the Las Cumbres Observatory network and Liverpool Telescopes. The targets were selected from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project sample with reliable black-hole mass measurements from Hbeta reverberation mapping results. We measure significant lags between the UV and various optical griz bands using JAVELIN and CREAM methods. We use the significant lag results from both methods to fit the accretion-disk structure using a Markov chain Monte Carlo approach. We study the accretion disk as a function of disk normalization, temperature scaling, and efficiency. We find direct evidence for diffuse nebular emission from Balmer and FeII lines over discrete wavelength ranges. We also find that our best-fit disk color profile is broadly consistent with the Shakura \& Sunyaev disk model. We compare our UV-optical lags to the disk sizes inferred from optical-optical lags of the same quasars and find that our results are consistent with these quasars being drawn from a limited high-lag subset of the broader population. Our results are therefore broadly consistent with models that suggest longer disk lags in a subset of quasars, for example, due to a nonzero size of the ionizing corona and/or magnetic heating contributing to the disk response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".