How Does the Polar Dust Affect the Correlation between Dust Covering Factor and Eddington Ratio in Type 1 Quasars Selected from the Sloan Digital Sky Survey Data Release 16?
Bibliographic record
Abstract
Abstract We revisit the dependence of the covering factor (CF) of dust torus on physical properties of active galactic nuclei (AGNs) by taking into account an AGN polar dust emission. The CF is converted from a ratio of infrared (IR) luminosity contributed from AGN dust torus ( ) and AGN bolometric luminosity (L bol), by assuming a nonlinear relation between luminosity ratio and intrinsic CF. We select 37,181 type 1 quasars at z < 0.7 from the Sloan Digital Sky Survey Data Release 16 quasar catalog. Their L bol, black hole mass (M BH), and Eddington ratio (λ Edd) are derived by spectral fitting with QSFit. We conduct spectral energy distribution decomposition by using X-CIGALE with a clumpy torus and polar dust model to estimate without being affected by the contribution of stellar and AGN polar dust to IR emission. For 5752 quasars whose physical quantities are securely determined, we perform a correlation analysis on CF and (i) L bol, (ii) M BH, and (iii) λ Edd. As a result, anticorrelations for CF–L bol, CF–M BH, and CF–λ Edd are confirmed. We find that incorporating the AGN polar dust emission makes those anticorrelations stronger compared to those without considering it. This indicates that polar dust wind probably driven by AGN radiative pressure is one of the key components to regulate obscuring material of AGNs.
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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 | 0.001 |
| 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) | 0.001 | 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".