Extremely red quasars from SDSS, BOSS and<i>WISE</i>: classification of optical spectra
Bibliographic record
Abstract
Quasars with extremely red infrared-to-optical colours are an interesting population that can test ideas about quasar evolution as well as orientation, obscuration and geometric effects in the so-called AGN unified model.To identify such a population, we match the quasar catalogues of the Sloan Digital Sky Survey (SDSS), the Baryon Oscillation Spectroscopic Survey (BOSS) to the Wide-Field Infrared Survey Explorer (WISE) to identify quasars with extremely high infrared-to-optical ratios.We identify 65 objects with r AB -W4 Vega > 14 mag (i.e.F ν (22 µm)/F ν (r) 1000).This sample spans a redshift range of 0.28 < z < 4.36 and has a bimodal distribution, with peaks at z ∼ 0.8 and z ∼ 2.5.It includes three z > 2.6 objects that are detected in the W4 band but not W1 or W2 (i.e.'W1W2 dropouts').The SDSS/BOSS spectra show that the majority of the objects are reddened type 1 quasars, type 2 quasars (both at low and high redshift) or objects with deep low-ionization broad absorption lines (BALs) that suppress the observed r-band flux.In addition, we identify a class of type 1 permitted broad emission-line objects at z 2-3 which are characterized by emission line rest-frame equivalent widths (REWs) of 150 Å, much larger than those of typical quasars.In particular, 55 per cent (45 per cent) of the non-BAL type 1s with measurable C IV in our sample have REW(C IV) > 100 (150) Å, compared to only 5.8 per cent (1.3 per cent) for non-BAL quasars in BOSS.These objects often also have unusual line ratios, such as very high N V/Ly α ratios.These large REWs might be caused by suppressed continuum emission analogous to type 2 quasars; however, there is no obvious mechanism in standard unified models to suppress the continuum without also obscuring the broad emission lines.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".