Comparing reflection and absorption models for the soft X-ray variability in the NLS1 AGN UGC 11763
Bibliographic record
Abstract
ABSTRACT We present a spectral analysis of two XMM–Newton observations of the narrow-line Seyfert 1 galaxy UGC 11763. UGC 11763 shows very different soft X-ray spectral shapes in the two observations separated by 12 yr. Three spectral models are considered to explain the multi-epoch X-ray variability of UGC 11763, one based on the relativistic disc reflection model, one based on multiple partially covering absorbers combined with the warm corona model, and a hybrid model. In the first model, the X-ray variability of UGC 11763 is caused by the emission from a compact coronal region with a variable size. The resulting disc reflection component changes accordingly. A warm absorption model with a modest column density is required in this model too. In the partially covering absorption scenario, the X-ray variability of UGC 11763 is caused by the variable covering factors of two absorbers located within a region of r < ≈100rg. Moreover, the temperature and strength of the warm corona have to change significantly too to explain the variable underlying soft X-ray emission. Lastly, we investigate the possibility of variable intrinsic power-law emission from the hot corona combined with variable absorption in UGC 11763 without changing the geometry of the corona in the third model. This hybrid model provides a slightly better fit than the partially covering absorption model with improvements in fitting the iron emission band. Current CCD-resolution data cannot distinguish these spectral models for UGC 11763. Future high-resolution X-ray missions, e.g. Athena and XRISM, will test them by resolving different spectral components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".