Bubbles and outflows: The novel JWST/NIRSpec view of the <i>z</i> = 1.59 obscured quasar XID2028
Bibliographic record
Abstract
Quasar feedback in the form of powerful outflows is invoked as a key mechanism to quench star formation in galaxies, although direct observational evidence is still scarce and debated. Here we present Early Release Science JWST NIRSpec IFU observations of the z = 1.59 prototypical obscured Active Galactic Nucleus (AGN) XID2028: This target represents a unique test case for studying quasar feedback at the peak epoch of AGN-galaxy co-evolution because extensive multi-wavelength coverage is available and a massive and extended outflow is detected in the ionised and molecular components. With the unprecedented sensitivity and spatial resolution of the JWST, the NIRSpec dataset reveals a wealth of structures in the ionised gas kinematics and morphology that were previously hidden in the seeing-limited ground-based data. In particular, we find evidence of an interaction between the interstellar medium of the galaxy and the quasar-driven outflow and radio jet that produces an expanding bubble from which the fast and extended wind detected in previous observations emerges. The new observations confirm the complex interplay between the AGN jet, wind and the interstellar medium of the host galaxy, highlighting the role of low-luminosity radio jets in AGN feedback. They also clearly show the new window that NIRSpec opens for detailed studies of feedback at high redshift.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".