Bibliographic record
Abstract
While the high-redshift quasar luminosity function closely parallels the hierarchical growth of dark matter halos, at lower redshifts quasars exhibit an antihierarchical turnoff, which moves from the most luminous objects to the faintest. We explore the idea that this may arise from self-regulating feedback, caused by quasar outflows. Using a hybrid approach that combines a detailed hydrodynamic simulation with observationally derived relationships, we calculate the luminosity function of quasars down to a redshift of z = 1 in a large, cosmologically representative volume. Outflows are included explicitly by tracking halo mergers and driving shocks into the surrounding intergalactic medium, with an energy output equal to a fixed 5% fraction of the bolometric luminosity. Our results are in excellent agreement with measurements of the spatial distribution of quasars on both small and large scales, and we detect an intriguing excess of galaxy-quasar pairs at very short separations. Our results also reproduce an antihierarchical turnoff in the quasar luminosity function; however, this falls short of that observed, as well as that predicted by analogous semianalytic models. The difference can be traced to the treatment of gas heating within galaxies and the presence of in-shock cooling. The simulated galaxy cluster L X - T relationship is close to that observed for z ≈ 1 clusters, but the simulated galaxy groups at z = 1 are significantly perturbed by quasar outflows. Measurements of anomalously high X-ray emission in high-redshift groups, along with detections of 1000 km s -1 winds in poststarburst ellipticals, would provide definitive evidence for the AGN-heating hypothesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".