Quantifying the AGN-driven outflows in ULIRGs (QUADROS) IV: HST/STIS spectroscopy of the sub-kpc warm outflow in F14394+5332
Bibliographic record
Abstract
ABSTRACT Considerable uncertainties remain about the nature of warm, AGN-driven outflows and their impact on the evolution of galaxies. This is because the outflows are often unresolved in ground-based observations. As part of a project to study the AGN outflows in some of the most rapidly evolving galaxies in the local Universe, here we present Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph (STIS) observations of F14394+5332E that resolve the sub-kpc warm outflow for the first time in an ultra-luminous infrared galaxy. The observations reveal a compact, high-ionization outflow region (rmax ∼ 0.9 kpc) set in a more extensive (rmax ∼ 1.4 kpc) halo that is kinematically quiescent and has a lower ionization state. A large line width (600 < FWHM < 1500 km s−1) is measured throughout the outflow region, and the outflowing gas shows a steep velocity gradient with radius, with the magnitude of the blueshifted velocities increasing from ∼500 to 1800 km s−1 from the inner to the outer part of the outflow. We interpret the observations in terms of the local acceleration, and hydrodynamic destruction, of dense clouds as they are swept up in a hot, low-density wind driven by the AGN. We discuss the implications for measuring the mass outflow rates and kinetic powers for the AGN-driven outflows in such objects.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".