Absence of radio-bright dominance in a near-infrared selected sample of red quasars
Bibliographic record
Abstract
Context. The dichotomy between red and blue quasars is still an open question. It is debated whether red quasars are simply blue quasars that are observed at certain inclination angles or if they provide insight into a transitional phase in the evolution of quasars. Aims. We investigate the relation between quasar colors and radio-detected fraction because radio observations of quasars provide a powerful tool in distinguishing between quasar models. Methods. We present the eHAQ+GAIA23 sample, which contains quasars from the High A(V) Quasar (HAQ) Survey, the Extended High A(V) Quasar (eHAQ) Survey, and the Gaia quasar survey. All quasars in this sample have been found using a near-infrared color selection of target candidates that have otherwise been missed by the Sloan Digital Sky Survey (SDSS). We implemented a redshift-dependent color cut in g * − i * to select red quasars in the sample and divided them into redshift bins, while using a nearest-neighbors algorithm to control for luminosity and redshift differences between our red quasar sample and a selected blue sample from the SDSS. Within each bin, we cross-matched the quasars to the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey and determined the radio-detection fraction. Results. For redshifts 0.8 < z ≤ 1.5, the red and blue quasars have a radio-detection fraction of 0.153 −0.032 +0.037 and 0.132 −0.030 +0.034 , respectively. The red and blue quasars with redshifts 1.5 < z ≤ 2.4 have radio-detection fractions of 0.059 −0.016 +0.019 and 0.060 −0.016 +0.019 , respectively, and the red and blue quasars with redshifts z > 2.4 have radio-detection fractions of 0.029 −0.012 +0.017 and 0.058 −0.019 +0.024 , respectively. For the WISE color-selected red quasars, we find a radio-detection fraction of 0.160 −0.034 +0.038 for redshifts 0.8 < z ≤ 1.5, 0.063 −0.017 +0.020 for redshifts 1.5 < z ≤ 2.4, and 0.051 −0.022 +0.030 for redshifts z > 2.4. In other words, we find similar radio-detection fractions for red and blue quasars within < 1 σ uncertainty, independent of redshift. This disagrees with what has been found in the literature for red quasars in SDSS. It should be noted that the fraction of broad absorption line (BAL) quasars in red SDSS quasars is about five times lower. BAL quasars have been observed to be more frequently radio quiet than other quasars, therefore the difference in BAL fractions could explain the difference in radio-detection fraction. Conclusions. The dusty torus of a quasar is transparent to radio emission. When we do not observe a difference between red and blue quasars, it leads us to argue that orientation is the main cause of quasar redness. Moreover, the observed higher proportion of BAL quasars in our dataset relative to the SDSS sample, along with the higher rate of radio detections, indicates an association of the redness of quasars and the inherent BAL fraction within the overall quasar population. This correlation suggests that the redness of quasars is intertwined with the inherent occurrence of BAL quasars within the entire population of quasars. In other words, the question why some quasars appear red or exhibit BAL characteristics might not be isolated; it could be directly related to the overall prevalence of BAL quasars in the quasar population. This finding highlights the need to explore the underlying factors contributing to both the redness and the frequency of BAL quasars, as they appear to be interconnected phenomena.
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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 | 0.000 |
| 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) | 0.003 | 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".