The Physical Properties of Low-redshift FeLoBAL Quasars. I. Spectral-synthesis Analysis of the Broad Absorption-line (BAL) Outflows Using SimBAL
Bibliographic record
Abstract
Abstract We present the first systematic study of 50 low-redshift (0.66 < z < 1.63) iron low-ionization broad absorption-line quasars (FeLoBALQs) using SimBAL, which represents a more than five-fold increase in the number of FeLoBALQs with detailed absorption line spectral analyses. We found the outflows have a wide range of ionization parameters, − 4 ≲ log U ≲ 1.2 and densities, 2.8 ≲ log n ≲ 8 [ cm − 3 ] . The objects in our sample showed FeLoBAL gas located at a wide range of distances 0 ≲ log R ≲ 4.4 [pc], although we do not find any evidence for disk winds (with R ≪ 0.01 pc) in our sample. The outflow strength primarily depends on the outflow velocity with faster outflows found in quasars that are luminous or that have flat or redder spectral energy distributions. We found that ∼18% of the FeLoBALQs in the sample have the significantly powerful outflows needed for quasar feedback. Eight objects showed overlapping troughs in the spectra, and we identified eleven loitering outflow objects, a new class of FeLoBALQs that are characterized by low outflow velocities and high column density winds located log R ≲ 1 [pc] from the central engine. The FeLoBALs in loitering outflows objects do not show properties expected for radiatively driven winds, and these objects may represent a distinct population among FeLoBALQs. We discuss how the potential acceleration mechanisms and the origins of the FeLoBAL winds may differ for outflows at different locations in quasars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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".