Obscuration in high-redshift jetted quasi-stellar objects
Bibliographic record
Abstract
Context. Obscuration in high-redshift quasi-stellar objects (QSOs) has a profound impact on our understanding of the evolution of supermassive black holes across cosmic time. An accurate quantification of its relevance is therefore mandatory. Aims. We present a study aimed at evaluating the importance of obscuration in high-redshift jetted QSOs, that is, active nuclei characterised by the presence of powerful relativistic jets. Methods. We compared the observed number of radio-detected QSOs at different radio flux density limits with the value predicted by the beaming model on the basis of the number of oriented sources (blazars). Any significant deficit between observations and predictions of radio-detected QSOs can be caused by the presence of obscuration along large angles from the jet direction. We applied this approach to two sizeable samples characterised by the same optical limit (mag = 21) but with significantly different radio density limits (30 mJy and 1 mJy, respectively) and containing a total of 87 independent radio-loud 4 ≤ z ≤ 6.8 QSOs, 31 of which are classified as blazars. Results. We found generally good agreement between the numbers predicted by the model and those actually observed, with only a marginal discrepancy at ∼0.5 mJy that could be caused by the sample’s lack of completeness. We concluded that we have no evidence of obscuration within angles 10–20° from the relativistic jet direction. We also discuss how the ongoing deep wide-angle radio surveys will be instrumental to testing the presence of obscuration at much larger angles, up to 30–35°. Finally, we suggest that, depending on the actual fraction of obscured QSOs, relativistic jets could be much more common at high redshifts compared to what is usually observed in the local Universe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".