Characterizing quasars in the mid-infrared: high signal-to-noise ratio spectral templates
Bibliographic record
Abstract
Mid-infrared (MIR) quasar spectra exhibit a suite of emission features including high ionization coronal lines from the narrow-line region illuminated by the ionizing continuum, broad dust bumps from silicates and graphites, and polycyclic aromatic hydrocarbon (PAH) features from star formation in the host galaxy. However, in Spitzer Infared Spectrograph (IRS) data, few features are detected in most individual spectra because of typically low signal-to-noise ratios (S/N). By generating spectral composites from over 180 IRS observations of Sloan Digital Sky Survey broad-line quasars, we boost the S/N and reveal features in the complex spectra that are otherwise lost in the noise. In addition to an overall composite, we generate composites in three different luminosity bins that span the range of 5.6 μm luminosities of 1040–1046 (erg s−1). We detect the high-ionization, forbidden emission lines of [S iv], [O iv] and [Ne v] λ14 μm in all templates and PAH features in all but the most luminous template. Ratios of lines with a range of ionization potentials show no evidence for a strong difference in the shape of the 41–97 eV ionizing continuum over this range of luminosities. The scaling of the emission-line luminosities as a function of continuum luminosity is consistent with what is expected, and shows no indication of a ‘disappearing narrow-line region’. The broad 10 and 18 μm silicate features in emission increase in strength with increasing luminosity, and a broad 3–5 μm blackbody consistent with graphite emission at 750 K is evident in the highest luminosity template. We find that the intrinsic quasar continua for all luminosity templates are consistent; apparent differences arise primarily from host galaxy contamination most evident at low luminosity.
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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 | 0.002 |
| 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 | 0.001 | 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".