Uncovering the relationship between ultra-fast outflows and the X-ray region in active galactic nuclei
Bibliographic record
Abstract
Ultra-fast outflows (UFOs), with outflow velocities of ~0.1-0.5c, have been detected in the 7-10 keV band of ~40% of sampled active galactic nuclei (AGNs) over the past decade. These high column density, highly ionised winds are thought to be driven by the central X-ray source via a radiation pressure or magnetohydrodynamic driving mechanism, launched from within ~100 rg of the black hole. There is, however, not a clear understanding of how the X-ray region and UFO are related, a crucial component in understanding how this mode of AGN feedback operates to significantly alter the evolutionary course of the host galaxy. To probe this relationship, we examine a sample of 20 Type 1 AGNs that were reported as having significant (>95% confidence) UFO detections in the literature. Using XMM-Newton EPIC-pn data across the broad 0.3-10 keV X-ray band, we implement a physically motivated reflection model to describe the underlying continuum. This method reveals that 4 AGNs are inconsistent with exhibiting a UFO, and finds several high-significance correlations of wind ionisation and column density with accretion rate, X-ray luminosity, disc inclination, and reflection fraction. Wind velocity is found to be more difficult to characterise. We perform XRISM Resolve and ATHENA X-IFU simulations to showcase the vast improvements in UFO science that will be achieved with data collected from these future missions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".