CONSTRAINING THE RADIO-LOUD FRACTION OF QUASARS AT<i>z</i>> 5.5
Bibliographic record
Abstract
Radio-loud active galactic nuclei at are typically located in dense environments and their host galaxies are among the most massive systems at those redshifts, providing key insights for galaxy evolution. Finding radio-loud quasars at the highest accessible redshifts ( ) is important to the study of their properties and environments at even earlier cosmic time. They could also serve as background sources for radio surveys intended to study the intergalactic medium beyond the epoch of reionization in H I 21 cm absorption. Currently, only five radio-loud ( ) quasars are known at . In this paper we search for quasars by cross–matching the optical Panoramic Survey Telescope & Rapid Response System 1 and radio Faint Images of the Radio Sky at Twenty cm surveys. The radio information allows identification of quasars missed by typical color-based selections. While we find no good quasar candidates at the sensitivities of these surveys, we discover two new radio-loud quasars at . Furthermore, we identify two additional radio-loud quasars that were not previously known to be radio-loud, nearly doubling the current sample. We show the importance of having infrared photometry for quasars to robustly classify them as radio-quiet or radio-loud. Based on this, we reclassify the quasar J0203+0012 ( z = 5.72), previously considered radio-loud, to be radio-quiet. Using the available data in the literature, we constrain the radio-loud fraction of quasars at , using the Kaplan–Meier estimator, to be . This result is consistent with there being no evolution of the radio-loud fraction with redshift, in contrast to what has been suggested by some studies at lower redshifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".