A colourful analysis: Probing the eclipse of the black hole and central engine in NGC 6814 using X-ray colour–colour grids
Bibliographic record
Abstract
ABSTRACT Eclipsing of the X-ray emitting region in active galactic nuclei (AGN) is a potentially powerful probe to examine the AGN environment and absorber properties. Here, we study the eclipse data from the 2016 XMM–Newton observation of NGC 6814 using a colour–colour analysis. Colours (i.e. hardness ratios) can provide the advantage of better time resolution over spectral analysis alone. Colour–colour grids are constructed to examine the effects of different parameters on the observed spectral variability during the eclipse. Consistent with previous spectral analysis, the variations are dominated by changes in the column density and covering fraction of the absorber. However, during maximum eclipse the behaviour of the absorber changes. Just after ingress, the eclipse is described by changes in column density and covering fraction, but prior to egress, the variations are dominated by changes in column density alone. Simulations are carried out to consider possible absorber geometries that might produce this behaviour. The behaviour is inconsistent with a single homogeneous cloud, but simulations suggest that multiple clouds, perhaps embedded in a highly ionized halo, could reproduce the results. In addition, we determine the orbital covering factor (fraction of orbital path-length) based on evidence of several eclipses in the 2016; 64-d Swift light curve. We estimate that ∼2–4 per cent of the orbit is covered by obscuring clouds and that the distribution of clouds is not isotropic.
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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 | 0.000 |
| 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".