Hα Time Delays of Active Galactic Nuclei from the Zwicky Transient Facility Broadband Photometry
Bibliographic record
Abstract
Abstract In our previous work on broadband photometric reverberation mapping (PRM), we proposed the interpolated cross-correlation function (ICCF)-Cut process to obtain the time lags of the Hα emission line from two broadband lightcurves via subtracting the continuum emission from the line band. Extending the work, we enlarge our sample to the Zwicky Transient Facility (ZTF) database. We adopt two criteria to select 123 type 1 active galactic nuclei (AGNs) with sufficient variability and smooth light curves from 3537 AGNs at z < 0.09 with more than 100 epoch observations in the g and r bands from the ZTF database. We calculate the Hα time lags for 23 of them that have previous spectroscopic reverberation mapping (SRM) results using the ICCF-Cut, Just Another Vehicle for Estimating Lags In Nuclei (JAVELIN), and χ 2 methods. Our obtained Hα time lags are slightly larger than the Hβ time lags, which is consistent with the previous SRM results and the theoretical model of the AGN broad-line region. The comparisons between the SRM and PRM lag distributions and between the subtracted emission line light curves indicate that after selecting AGNs with the two criteria, combining the ICCF-Cut, JAVELIN, and χ 2 methods provides an efficient way to get the reliable Hα lags from the broadband PRM. Such techniques can be used to estimate the black hole masses of a large sample of AGNs in large multiepoch photometric sky surveys such as the Legacy Survey of Space and Time and the survey from the Wide Field Survey Telescope in the near future.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".