On the incidence of weak and strong Mg <scp>ii</scp> absorbers towards the flat- and steep-spectrum radio quasars
Bibliographic record
Abstract
ABSTRACT The incidence rate of Mg ii absorbers per unit redshift path ($\mathrm{ d}N/\mathrm{ d}z$) towards quasars’ sightline has been used to probe the interaction between quasar jets and surrounding gas clouds. Studies using core- and lobe-dominated samples found a higher $\mathrm{ d}N/\mathrm{ d}z$ for strong Mg ii absorbers (rest equivalent width, $W_{r}(2796)\ge ~1.0~\rm{\mathring{\rm A}}$) in velocity offsets in the range 5000 $\text{km}\, \text{s}^{-1}$$<\beta c <~$60 000 $\text{km}\, \text{s}^{-1}$ (with $\beta \equiv v/c$), towards core-dominated sources. In this study, we applied a stringent spectral index criterion: $\alpha _{\text{radio}}~< -0.7$ for steep-spectrum radio quasars (SSRQs) and $\alpha _{\text{radio}} >-0.3~$ for flat-spectrum radio quasars (FSRQs). Using this, we assembled the largest sample till date – 441 FSRQs and 464 SSRQs with suitable optical spectra – to study both strong absorbers and weak ($0.3~\rm{\mathring{\rm A}}< W_r (2796)< 1.0$ Å) Mg ii absorbers. We conducted a detailed comparison of absorbers’ incidence rate and offset velocity distributions. Our main findings are as follows: (i) For both weak and strong absorbers, we found no significant excess in $\mathrm{ d}N/\mathrm{ d}z$ towards FSRQ compared to SSRQ sightlines. (ii) The $\mathrm{ d}N/\mathrm{ d}\beta$ distribution of Mg ii absorbers along FSRQs and SSRQs is statistically similar. (iii) The cumulative distribution of weak Mg ii absorbers is slightly lower for $\beta < 0.3$, but shows an excess at higher $\beta$. This suggests that, while intrinsic Mg ii absorber abundance is comparable along both sightlines, FSRQs’ more aligned relativistic jets cluster weak absorbers at high $\beta$, consistent with the scenario of jet-driven acceleration of smaller gas clumps.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".