Spectral Energy Distributions and Multiwavelength Selection of Type 1 Quasars
Bibliographic record
Abstract
We present an analysis of the mid-infrared (MIR) and optical properties of type 1 (broad-line) quasars detected by the Spitzer Space Telescope . The MIR color-redshift relation is characterized to z ~ 3, with predictions to z = 7. We demonstrate how combining MIR and optical colors can yield even more efficient selection of active galactic nuclei (AGNs) than MIR or optical colors alone. Composite spectral energy distributions (SEDs) are constructed for 259 quasars with both Sloan Digital Sky Survey and Spitzer photometry, supplemented by near-IR, GALEX , VLA, and ROSAT data, where available. We discuss how the spectral diversity of quasars influences the determination of bolometric luminosities and accretion rates; assuming the mean SED can lead to errors as large as 50% for individual quasars when inferring a bolometric luminosity from an optical luminosity. Finally, we show that careful consideration of the shape of the mean quasar SED and its redshift dependence leads to a lower estimate of the fraction of reddened/obscured AGNs missed by optical surveys as compared to estimates derived from a single mean MIR to optical flux ratio.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".