High Signal-to-Noise Ratio Mid-Infrared Quasar Spectral Templates
Bibliographic record
Abstract
Abstract Mid-infrared (MIR) quasar spectra exhibit a suite of emission features including high ionization coronal lines from the narrow line region (NLR) illuminated by the ionizing continuum, and hot dust features from grains, as well as polycyclic aromatic hydrocarbons (PAH) features from star formation in the host galaxy. Few features are detected in most spectra because of typically low signal-to-noise ratio (S/N) data. By generating spectral composites in three different luminosity bins from over 180 Spitzer Ifnfrared Spectrograph (IRS) observations, we boost the S/N and reveal important features in the complex spectra. We detect high-ionization, forbidden emission lines in all templates, PAH features in all but the most luminous objects, and broad silicate and graphite features in emission whose strength increases relative to the continuum with luminosity. We find that the intrinsic quasar spectrum for all luminosity templates is consistent, and the differences in the spectra can be explained by host galaxy contamination in the lower luminosity templates. We also posit that star formation may be active in most quasar host galaxies, but the spectral features of star formation are only detectable if the quasar is faint.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".