The Diversity of Environments around Luminous Quasars at Redshift z ∼ 6
Bibliographic record
Abstract
Abstract Significant clustering around the rarest luminous quasars is a feature predicted by dark matter theory combined with number density matching arguments. However, this expectation is not reflected by observations of quasars residing in a diverse range of environments. Here we assess the tension in the diverse clustering of visible i-band dropout galaxies around luminous z ∼ 6 quasars. Our approach uses a simple empirical method to derive the median luminosity–to–halo mass relation, L c (M h ), for both quasars and galaxies under the assumption of lognormal luminosity scatter, Σ Q and Σ G . We show that higher Σ Q reduces the average halo mass hosting a quasar of a given luminosity, thus introducing at least a partial reversion to the mean in the number count distribution of nearby Lyman-break galaxies. We generate a large sample of mock Hubble Space Telescope fields of view centered across rare z ∼ 6 quasars by resampling pencil beams traced through the dark matter component of the BlueTides cosmological simulation. We find that diverse quasar environments are expected for Σ Q > 0.4, consistent with numerous observations and theoretical studies. However, we note that the average number of galaxies around the central quasar is primarily driven by galaxy evolutionary processes in neighboring halos, as embodied by our parameter Σ G , instead of a difference in the large-scale structure around the central quasar host, embodied by Σ Q . We conclude that models with Σ G > 0.3 are consistent with current observational constraints on high-z quasars, and that such a value is comparable to the scatter estimated from hydrodynamical simulations of galaxy formation.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".