<i>Herschel</i>-ATLAS: the far-infrared properties and star formation rates of broad absorption line quasi-stellar objects
Bibliographic record
Abstract
We have used data from the Herschel Astrophysical Terahertz Large-Area Survey (H-ATLAS) at 250, 350 and 500 μm to determine the far-infrared (FIR) properties of 50 broad absorption line quasars (BAL QSOs). Our sample contains 49 high-ionization BAL QSOs (HiBALs) and one low-ionization BAL QSO (LoBAL) which are compared against a sample of 329 non-BAL QSOs. These samples are matched over the redshift range 1.5 ≤ z < 2.3 and in absolute i-band magnitude over the range −28 ≤ Mi ≤ −24. Of these, three BAL QSOs (HiBALs) and 27 non-BAL QSOs are detected at the >5 σ level. We calculate star formation rates (SFRs) for our individually detected HiBAL QSOs and the non-detected LoBAL QSO as well as average SFRs for the BAL and non-BAL QSO samples based on stacking the Herschel data. We find no difference between the HiBAL and non-BAL QSO samples in the FIR, even when separated based on differing BAL QSO classifications. Using Mrk 231 as a template, the weighted mean SFR is estimated to be ≈240 ± 21 M⊙ yr−1 for the full sample, although this figure should be treated as an upper limit if active galactic nucleus (AGN)-heated dust makes a contribution to the FIR emission. Despite tentative claims in the literature, we do not find a dependence of c iv equivalent width on FIR emission, suggesting that the strength of any outflow in these objects is not linked to their FIR output. These results strongly suggest that BAL QSOs (more specifically HiBALs) can be accommodated within a simple AGN unified scheme in which our line of sight to the nucleus intersects outflowing material. Models in which HiBALs are caught towards the end of a period of enhanced spheroid and black hole growth, during which a wind terminates the star formation activity, are not supported by the observed FIR properties.
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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 | 0.001 | 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) | 0.003 | 0.002 |
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".