MINING FOR DUST IN TYPE 1 QUASARS
Bibliographic record
Abstract
We explore the extinction/reddening of ˜35,000 uniformly selected quasars with 0\\lt z≤slant 5.3 in order to better understand their intrinsic optical/ultraviolet (UV) spectral energy distributions. Using rest-frame optical-UV photometry taken from the Sloan Digital Sky Survey’s (SDSS) 7th data release, cross-matched to WISE in the mid-infrared, 2MASS and UKIDSS in the near-infrared, and GALEX in the UV, we isolate outliers in the color distribution and find them well described by an SMC-like reddening law. A hierarchical Bayesian model with a Markov Chain Monte Carlo sampling method was used to find distributions of power law indices and E(B-V) consistent with both the broad absorption line (BAL) and non-BAL samples. We find that, of the ugriz color-selected type 1 quasars in SDSS, 2.5% (13%) of the non-BAL (BAL) sample are consistent with E(B-V)\\gt 0.1 and 0.1% (1.3%) with E(B-V)\\gt 0.2. Simulations show both populations of quasars are intrinsically bluer than the mean composite, with a mean spectral index ({{α }λ }) of -1.79 (-1.83). The emission and absorption-line properties of both samples reveal that quasars with intrinsically red continua have narrower Balmer lines and stronger high-ionization emission lines, the latter indicating a harder continuum in the extreme-UV and the former pointing to differences in black hole mass and/or orientation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".