The SDSS-V Black Hole Mapper Reverberation Mapping Project: A Kinematically Variable Broad-line Region and Consequences for the Masses of Luminous Quasars
Bibliographic record
Abstract
Abstract We present a velocity-resolved reverberation mapping analysis of the hypervariable quasar RM160 (SDSS J141041.25+531849.0) at z = 0.359 with 153 spectroscopic epochs of data representing a 10 yr baseline (2013–2023). We split the baseline into two regimes based on the 3× flux increase in the light curve: a “low state” phase during the years 2013–2019 and a “high state” phase during the years 2022–2023. The velocity-resolved lag profiles (VRLPs) indicate that gas with different kinematics dominates the line emission in different states. The H β VRLP begins with a signature of inflow onto the broad-line region (BLR) in the low state, while in the high state it is flatter with less signature of inflow. The H α VRLP begins consistent with a virialized BLR in the low state, while in the high state shows a signature of inflow. The differences in the kinematics between the Balmer lines and between the low state and the high state suggests complex BLR dynamics. We find that the BLR radius and velocity (both FWHM and σ ) do not obey a constant virial product throughout the monitoring period. We find that the BLR lags and continuum luminosity are correlated, consistent with rapid response of the BLR gas to the illuminating continuum. The BLR kinematic profile changes in unpredictable ways that are not related to continuum changes and reverberation lag. Our observations indicate that nonvirial kinematics can significantly contribute to observed line profiles, suggesting caution for black hole mass estimation in luminous and highly varying quasars like RM160.
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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 | 0.002 |
| 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 | 0.000 |
| 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".