Tracking the year-to-year variation in the spectral energy distribution of the narrow-line Seyfert 1 galaxy Mrk 335
Bibliographic record
Abstract
ABSTRACT Multiwavelength monitoring of Mrk 335 with Swift between 2007 and 2019 are used to construct annual spectral energy distributions (SEDs) and track year-to-year changes. Non-contemporaneous archival data prior to 2007 are used to build a bright state SED. In this work, the changes are examined and quantified to build the foundation for future SED modelling. The yearly SEDs trace a downward trend on the average, with the X-ray portion varying significantly and acquiring further lower values in the past two years when compared to the optical/UV portion of SED. The bolometric Eddington ratios derived using optical/UV to X-ray SEDs and the calculated X-ray luminosities show a gradual decrease over the monitoring period. Changes in the parameters over time are examined. Principal component analysis suggests that the primary variability is in the X-ray properties of Mrk 335. When looking at the broader picture of Mrk 335 and its behaviour, the X-rays, accounting most of the variability in the 13-yr data, are possibly driven by physical processes related to the corona or absorption whereas the modest optical–UV variations suggest their origin within the accretion disc. These results are consistent with the previous interpretation of Mrk 335 using the timing analyses on the monitoring data and spectral modelling of deep observations.
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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".