Observing the Effect of Galaxy Environment on the Evolution of Active Galactic Nuclei in the Era of Multiplexed Wide-Field Fibre-Optic Spectroscopy
Bibliographic record
Abstract
In this thesis, we use the environment of active galactic nuclei (AGN) to test AGN unification. Furthermore, we analyse how interaction between the environment and host galaxy can influence AGN evolution. This is done for a range of galaxy densities, from the field to the cores of galaxy clusters. We utilise the high spectroscopic completeness of the Galaxy And Mass Assembly survey (GAMA) to perform a comparative analysis of the pair-wise environments of broad- and narrow-line AGN. Our observations show no difference in the frequency of broad- and narrow-line AGN in all but the tightest galaxy pairs. Furthermore, our observations show no difference in the colour or star-forming properties of the neighbouring galaxies of AGN, contrary to previous works. These observations are consistent with AGN unification and indicate that close galaxy interactions may increase nuclear obscuration. Optically selected AGN preferentially inhabit infall regions of massive clusters, and are rarely found in the cluster core. Using the depth and completeness of GAMA, we optically select 451 AGN from 695 groups in the halo mass range 11.5 < log_{10}(M_{200}/M_{\odot}) < 14.5 at z < 0.15. At log_{10}(M_{200}/M_{\odot}) > 13:5 our results are similar to previous works on clusters with a deficit of AGN observed in the group core at 3:6 confidence. At lower halo masses no preferred location for AGN is seen. Weakly accreting radio AGN are found in dense galactic environments, and minor-mergers have been invoked as a plausible mechanism to ‘drip-feed’ these low-excitation radio galaxies (LERGs). We use deep optical imaging from the Dark Energy Camera Legacy Survey (DECaLS) to search for low-surface brightness tidal remnants in a sample of 189 LERGs. We observe such tidal features in 27+3.5/-3.0 per cent of LERGs compared to 31.5+2.5/-2.3 per cent of a control sample. Our results are thus inconsistent with the hypothesis that minor-mergers play a significant role in LERG fuelling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".