Imaging of SDSS<i>z</i>> 6 Quasar Fields: Gravitational Lensing, Companion Galaxies, and the Host Dark Matter Halos
Bibliographic record
Abstract
We have undertaken deep optical imaging observations of three 6.2 < z < 6.5 quasar fields in the i ' and z ' filters. These data are used to search for foreground galaxies that are gravitationally lensing the quasars and distant galaxies physically associated with the quasars. Foreground galaxies are found closer than 5'' to the lines of sight of two of the three quasars. However, the faintness of these galaxies suggests that they have fairly low masses and provide only weak magnifications (μ ≲ 1.1). No convincing galaxies physically associated with the quasars are found, and the number of i '-band dropouts is consistent with that found in random fields. We consider the expected dark matter halo masses that host these quasars under the assumption that a correlation between black hole mass and dark matter halo mass exists. We show that the steepness of the high-mass tail of the halo mass function at this redshift, combined with realistic amounts of scatter in this correlation, leads to expected halo masses substantially lower than previously believed. This analysis can explain the lack of companion galaxies found here and the low dynamical mass recently published for one of the quasars.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".