Quasar clustering at redshift 6
Bibliographic record
Abstract
Context. Large-scale surveys over the last years have revealed about 300 quasi-stellar objects (QSOs) at redshifts above 6. Follow-up observations have identified surprising properties, such as the very high black hole (BH) masses, spatial correlations with surrounding cold gas of the host galaxy, and high CIV-MgII Velocity shifts. In particular, the discovery of luminous high-redshift quasars suggests that at least some BHs likely have high masses at birth and grow efficiently. Aims. Our aim is to quantify quasar pairs at high redshift for a large sample of objects. This provides a new key constraint on a combination of parameters related to the origin and assembly for the most massive BHs: formation efficiency and clustering, growth efficiency, and the relative contribution of BH mergers. Methods. We observed 116 spectroscopically confirmed QSOs around redshift 6 with the simultaneous seven-channel imager Gamma-ray Burst Optical/Near-infrared Detector in order to search for companions. Applying colour-colour cuts identical to those which led to the spectroscopically confirmed QSOs, we performed Le PHARE fits to the 26 best QSO pair candidates, and obtained spectroscopic observations for 11 of them. Results. We do not find any QSO pair with a companion brighter than M1450(AB) < −26 mag within our 0.1–3.3 h−1 cMpc search radius, in contrast to the serendipitous findings in the redshift range 4–5. However, a small fraction of such pairs at this luminosity and redshift is consistent with indications from present-day cosmological-scale galaxy evolution models. In turn, the incidence of L- and T-type brown dwarfs, which occupy a similar colour space to z ∼ 6 QSOs, is higher than expected, by a factor of 5 and 20, respectively.
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".