The extended Baryon Oscillation Spectroscopic Survey: Variability selection and quasar luminosity function
Bibliographic record
Abstract
The extended Baryon Oscillation Spectroscopic Survey of the Sloan Digital Sky Survey (SDSS-IV/eBOSS) has an extensive quasar program that combines several selection methods. Among these, the photometric variability technique provides highly uniform samples, which are unaffected by the redshift bias of traditional optical-color selections, when z = 2.7−3.5 quasars cross the stellar locus or when host galaxy light affects quasar colors at z< 0.9. We present the variability selection of quasars in eBOSS, focusing on a specific program that led to a sample of 13 876 quasars to gdered = 22.5 over a 94.5 deg2 region in Stripe 82, which has an areal density 1.5 times higher than over the rest of the eBOSS footprint. We use these variability-selected data to provide a new measurement of the quasar luminosity function (QLF) in the redshift range of 0.68 <z< 4.0. Our sample is denser and reaches more deeply than those used in previous studies of the QLF, and it is among the largest ones. At the faint end, our QLF extends to Mg(z = 2) = −21.80 at low redshift and to Mg(z = 2) = −26.20 at z ~ 4. We fit the QLF using two independent double-power-law models with ten free parameters each. The first model is a pure luminosity-function evolution (PLE) with bright-end and faint-end slopes allowed to be different on either side of z = 2.2. The other is a simple PLE at z< 2.2, combined with a model that comprises both luminosity and density evolution (LEDE) at z> 2.2. Both models are constrained to be continuous at z = 2.2. They present a flattening of the bright-end slope at high redshift. The LEDE model indicates a reduction of the break density with increasing redshift, but the evolution of the break magnitude depends on the parameterization. The models are in excellent accord, predicting quasar counts that agree within 0.3% (resp., 1.1%) to g< 22.5 (resp., g< 23). The models are also in good agreement over the entire redshift range with models from previous studies.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".