Environment of SDSS Quasars at <i>z</i> = 0.4–1.0 Explored by Subaru HSC
Bibliographic record
Abstract
Abstract The relationship between quasars and their galaxy environment is important for understanding the evolution of galaxies and supermassive black holes, but it is not fully understood. We perform a wide and deep exploration of the environment of quasars at 0.4 < z < 1.0 using the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. We investigate the environment of the 1912 spectroscopically selected quasars from the Sloan Digital Sky Survey (SDSS), using photometrically selected galaxies from the HSC-SSP data, over an area of 505 deg 2 . The quasar environment is compared to the environment of matched galaxies with similar stellar mass and redshift. We employ the k -nearest neighbor method to define the local galaxy number density for both the quasars and the matched galaxies at a scale of a few hundred kpc. As a result, we find that the number density of galaxies around SDSS quasars is lower than that of the matched galaxies by ∼11%–20%. We also investigate possible correlations between the local galaxy number densities and the quasar properties such as black hole mass ( M BH ) and Eddington ratio ( R Edd ). We find that the number density is weakly correlated with M BH and only marginally with R Edd . The apparent correlation with M BH is likely driven by their common dependence on redshift rather than a direct physical connection, while the marginal trend with R Edd is statistically insignificant. These results suggest that the quasar activity is not triggered by the high number density of surrounding galaxies at the scale of a few hundred kpc.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".