The dust attenuation law in <i>z</i> ∼ 6 quasars
Bibliographic record
Abstract
ABSTRACT We investigate the attenuation law in z ∼ 6 quasars by combining cosmological zoom-in hydrodynamical simulations of quasar host galaxies, with multifrequency radiative transfer calculations. We consider several dust models differing in terms of grain-size distributions, dust mass, and chemical composition, and compare the resulting synthetic spectral energy distributions with data from bright, early quasars. We show that only dust models with grain-size distributions in which small grains ($a\lesssim 0.1~\mu {\rm m}$, corresponding to $\approx 60{{\ \rm per\ cent}}$ of the total dust mass) are selectively removed from the dusty medium provide a good fit to the data. Removal can occur if small grains are efficiently destroyed in quasar environments and/or early dust production preferentially results in large grains. Attenuation curves for these models are close to flat, and consistent with recent data; they correspond to an effective dust-to-metal ratio fd ≃ 0.38, i.e. close to the Milky Way value.
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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 | 0.000 | 0.000 |
| 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".