On the dust geometry in radio-loud active galactic nuclei
Bibliographic record
Abstract
We use photometric and spectroscopic infrared observations obtained with the Spitzer Space Telescope of 12 radio-loud active galactic nuclei to investigate the dust geometry. Our approach is to look at the change of the infrared spectral energy distribution (SED) and the strength of the 10-μm silicate feature with jet viewing angle. We find that (i) a combination of three or four blackbodies fits well the infrared SED, (ii) the sources viewed closer to the jet axis appear to have stronger warm (∼300–800 K) and cold (∼150–250 K) dust emissions relative to the hot component and (iii) the silicate features are always in emission and strongly redshifted. We test clumpy torus models and find that (i) they approximate well the mid-infrared part of the SED, but significantly underpredict the fluxes at both near- and far-infrared wavelengths; (ii) they can constrain the dust composition (in our case to that of the standard interstellar medium); (iii) they require relatively large (∼10–20 per cent the speed of light) redward displacements and (iv) they give robust total mass estimates, but are insensitive to the assumed geometry.
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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 | 0.001 |
| 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".