TRACING THE OUTFLOW OF A<i>z</i>= 0.334 FeLoBAL: NEW CONSTRAINTS FROM LOW-IONIZATION ABSORBERS IN FBQS J1151+3822
Bibliographic record
Abstract
We show for the first time that FBQS J1151+3822 is an iron low-ionization broad absorption line quasar (FeLoBAL QSO), the second-brightest and second-closest known example of this class. He i * and Fe ii together act as an effective analytical tool, allowing us to obtain useful kinematic constraints from photoionization models of the outflow without needing to assume any particular acceleration model. The main outflow's log ionization parameter is −1.5, the log hydrogen density (cm −3 ) 5.5–8, the log hydrogen column density (cm −2 ) 21.7–21.9, the absorption radius 7.2–127 pc, and the kinetic luminosity is 0.16%–4.5% of the bolometric luminosity. We obtain line-of-sight covering fractions of ∼0.25 for strong Fe ii , ∼0.5 for He i *, and ∼0.6 for Mg ii . Narrower and shallower absorption lines from weaker Fe ii and Mn ii with an outflow velocity of ∼3400 km s −1 have appeared between 2005 and 2011, suggesting that dense cores may have condensed inside the main outflow. Consideration of the literature might suggest that the FBQS J1151+3822 outflow is a member of a rare and distinct subclass of FeLoBALs with high densities and correspondingly small absorption radii. We find, however, that such outflows are not necessarily a distinct subclass, and that their apparent rarity could be a symptom of selection bias in studies using density-sensitive lines.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".