THE GALEX TIME DOMAIN SURVEY. II. WAVELENGTH-DEPENDENT VARIABILITY OF ACTIVE GALACTIC NUCLEI IN THE PAN-STARRS1 MEDIUM DEEP SURVEY
Bibliographic record
Abstract
ABSTRACT We analyze the wavelength-dependent variability of a sample of spectroscopically confirmed active galactic nuclei selected from near-UV (NUV) variable sources in the GALEX Time Domain Survey that have a large amplitude of optical variability (difference-flux S/N > 3) in the Pan-STARRS1 Medium Deep Survey (PS1 MDS). By matching GALEX and PS1 epochs in five bands (NUV, g P1, r P1, i P1, z P1) in time, and taking their flux difference, we create co-temporal difference-flux spectral energy distributions ( <?CDATA ${\rm{\Delta }}f\mathrm{SEDs}$?> ) using two chosen epochs for each of the 23 objects in our sample, on timescales of about a year. We confirm the “bluer-when-brighter” trend reported in previous studies, and measure a median spectral index of the <?CDATA ${\rm{\Delta }}f\mathrm{SEDs}$?> of <?CDATA ${\alpha }_{\lambda }$?> = 2.1 that is consistent with an accretion disk spectrum. We further fit the <?CDATA ${\rm{\Delta }}f\mathrm{SEDs}$?> of each source with a standard accretion disk model in which the accretion rate changes from one epoch to the other. In our sample, 17 out of 23 (∼74%) sources are described well by this variable accretion-rate disk model, with a median average characteristic disk temperature <?CDATA $\bar{T}* $?> of <?CDATA $1.2\times {10}^{5}$?> K that is consistent with the temperatures expected, given the distribution of accretion rates and black hole masses inferred for the sample. Our analysis also shows that the variable accretion rate model is a better fit to the <?CDATA ${\rm{\Delta }}f\mathrm{SEDs}$?> than a simple power law.
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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 | 0.001 |
| 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".