Measuring Column Densities in Quasar Outflows: VLT Observations of QSO 2359−1241
Bibliographic record
Abstract
We present high-resolution spectroscopic VLT observations of the outflow seen in QSO 2359–1241. These data contain absorption troughs from five resonance Fe II lines with a resolution of ~7 km s −1 and a signal-to-noise ratio per resolution element of order 100. We use this unprecedented high-quality data set to investigate the physical distribution of the material in front of the source and by that to determine the column densities of the absorbed troughs. We find that the apparent optical depth model gives a very poor fit to the data and greatly underestimates the column density measurements. Power-law distributions and partial covering models give much better fits, with some advantage to power-law models, while both models yield similar column density estimates. The better fit of the power-law model solves a long-standing problem plaguing the partial covering model when applied to large distance scale outflow: how to obtain a velocity-dependent covering factor for an outflow situated at distances thousands of time greater than the size of the AGN emission source. This problem does not affect power-law models. Therefore, based on the better fit and plausibility of the physical model, we conclude that in QSO 2359–1241, the outflow covers the full extent of the emission source but in a nonhomogeneous way.
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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".