Time variability of ultra fast BAL outflows using SALT: C <scp>iv</scp> equivalent width analysis
Bibliographic record
Abstract
ABSTRACT We study the time variability (over ≤7.3 yr) of ultra fast outflows (UFOs) detected in a sample of 64 C iv broad absorption line (BAL) quasars (with 80 distinct BAL components) monitored using the Southern African Large Telescope. By comparing the properties of the quasar in our sample with those of a control sample of non-BAL quasars, we show that the distributions of black hole mass are different and the bolometric luminosities and optical photometric variations of UFO BAL quasars are slightly smaller compared to that of non-BAL quasars. The detection fraction of C iv equivalent width (W) variability (∼95 per cent), the fractional variability amplitude $\left(\frac{\Delta W}{W}\right)$ and the fraction of ‘highly variable’ BAL (i.e. $\big|$$\frac{\Delta W}{W}$$\big|$ > 0.67) components (∼33 per cent) are higher in our sample compared to the general BAL population. The scatter in $\frac{\Delta W}{W}$ and the fraction of ‘highly variable’ BALs increase with the time-scale probed. The $\frac{\Delta W}{W}$ distribution is asymmetric at large time scales. We attribute this to the BAL strengthening time-scales being shorter than the weakening time-scales. The BAL variability amplitude correlates strongly with the BAL properties compared to the quasar properties. BALs with low W, high-velocity, shallow profiles, and low-velocity width tend to show more variability. When multiple BAL components are present, a correlated variability is seen between low- and high-velocity components with the latter showing a larger amplitude variations. We find an anticorrelation between the fractional variations in the continuum flux and W. While this suggests photoionization induced variability, the scatter in continuum flux is much smaller than that of W.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".