Absence of radio-bright dominance in a near-infrared selected sample of red quasars
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».