Nature and statistical properties of quasar associated absorption systems in the XQ-100 Legacy Survey
Bibliographic record
Abstract
We statistically study the physical properties of a sample of narrow absorption line (NAL) systems looking for empirical evidences to distinguish between intrinsic and intervening NALs without taking into account any a priori definition or velocity cut-off. We analyze the spectra of 100 quasars with 3.5 < z$\rm_{em}$ < 4.5, observed with X-shooter/VLT in the context of the XQ-100 Legacy Survey. We detect a $\sim$ 8 $\sigma$ excess in the number density of absorbers within 10,000 km/s of the quasar emission redshift with respect to the random occurrence of NALs. This excess does not show a dependence on the quasar bolometric luminosity and it is not due to the redshift evolution of NALs. It extends far beyond the standard 5000 km/s cut-off traditionally defined for associated absorption lines. We propose to modify this definition, extending the threshold to 10,000 km/s when also weak absorbers (equivalent width < 0.2 \AA) are considered. We infer NV is the ion that better traces the effects of the quasar ionization field, offering the best statistical tool to identify intrinsic systems. Following this criterion we estimate that the fraction of quasars in our sample hosting an intrinsic NAL system is 33 percent. Lastly, we compare the properties of the material along the quasar line of sight, derived from our sample, with results based on close quasar pairs investigating the transverse direction. We find a deficiency of cool gas (traced by CII) along the line of sight associated with the quasar host galaxy, in contrast with what is observed in the transverse direction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".