Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. XIII. Ultraviolet Time Lag of Hβ Emission in Mrk 142
Bibliographic record
Abstract
Abstract We performed a rigorous reverberation-mapping analysis of the broad-line region (BLR) in a highly accreting (L/L Edd = 0.74–3.4) active galactic nucleus, Markarian 142 (Mrk 142), for the first time using concurrent observations of the inner accretion disk and the BLR to determine a time lag for the Hβ λ4861 emission relative to the ultraviolet (UV) continuum variations. We used continuum data taken with the Niel Gehrels Swift Observatory in the UVW2 band, and the Las Cumbres Observatory, Dan Zowada Memorial Observatory, and Liverpool Telescope in the g band, as part of the broader Mrk 142 multiwavelength monitoring campaign in 2019. We obtained new spectroscopic observations covering the Hβ broad emission line in the optical from the Gemini North Telescope and the Lijiang 2.4 m Telescope for a total of 102 epochs (over a period of 8 months) contemporaneous to the continuum data. Our primary result states a UV-to-Hβ time lag of 8.68 − 0.72 + 0.75 days in Mrk 142 obtained from light-curve analysis with a Python-based running optimal average algorithm. We placed our new measurements for Mrk 142 on the optical and UV radius–luminosity relations for NGC 5548 to understand the nature of the continuum driver. The positions of Mrk 142 on the scaling relations suggest that UV is closer to the “true” driving continuum than the optical. Furthermore, we obtain log ( M • / M ⊙ ) = 6.32 ± 0.29 assuming UV as the primary driving continuum.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".