AGN properties of ~1 million member galaxies of galaxy groups and clusters at z < 1.4 based on the Subaru Hyper Suprime-Cam survey
Bibliographic record
Abstract
Herein, we present the statistical properties of active galactic nuclei (AGNs) for approximately 1 million member galaxies of galaxy groups and clusters, with 0.1 $<$ cluster redshift ($z_{\rm cl}$) $<$ 1.4, selected using Subaru Hyper Suprime-Cam, the so-called CAMIRA clusters. In this research, we focused on the AGN power fraction ($f_{\rm AGN}$), which is defined as the proportion of the contribution of AGNs to the total infrared (IR) luminosity, $L_{\rm IR}$ (AGN)/$L_{\rm IR}$, and examined how $f_{\rm AGN}$ depends on (i) $z_{\rm cl}$ and (ii) the distance from the cluster center. We compiled multiwavelength data using the ultraviolet--mid-IR range. Moreover, we performed spectral energy distribution fits to determine $f_{\rm AGN}$ using the CIGALE code with the SKIRTOR AGN model. We found that (i) the value of $f_{\rm AGN}$ in the CAMIRA clusters is positively correlated with $z_{\rm cl}$, with the correlation slope being steeper than that for field galaxies, and (ii) $f_{\rm AGN}$ exhibits a high value at the cluster outskirts. These results indicate that the emergence of AGN population depends on the redshift and environment and that galaxy groups and clusters at high redshifts are important in AGN evolution. Additionally, we demonstrated that cluster--cluster mergers may enhance AGN activity at the outskirts of particularly massive galaxy clusters. Our findings are consistent with a related study on the CAMIRA clusters that was based on the AGN number fraction.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".