The most luminous, merger-free AGNs show only marginal correlation with bar presence
Bibliographic record
Abstract
ABSTRACT The role of large-scale bars in the fuelling of active galactic nuclei (AGNs) is still debated, even as evidence mounts that black hole growth in the absence of galaxy mergers cumulatively dominates and may substantially influence disc (i.e. merger-free) galaxy evolution. We investigate whether large-scale galactic bars are a good candidate for merger-free AGN fuelling. Specifically, we combine slit spectroscopy and Hubble Space Telescope imagery to characterize star formation rates (SFRs) and stellar masses of the unambiguously disc-dominated host galaxies of a sample of luminous, Type-1 AGN with 0.02 < z < 0.24. After carefully correcting for AGN signal, we find no clear difference in SFRs between AGN hosts and a stellar mass-matched sample of galaxies lacking an AGN (0.013 < z < 0.19), although this could be due to small sample size (nAGN = 34). We correct for SFR and stellar mass to minimize selection biases, and compare the bar fraction in the two samples. We find that AGNs are marginally (∼1.7σ) more likely to host a bar than inactive galaxies, with AGN hosts having a bar fraction, $f_{\mathrm{bar}}=0.59^{+0.08}_{-0.09}$ and inactive galaxies having a bar fraction, $f_{\mathrm{bar}}=0.44^{+0.08}_{-0.09}$. However, we find no further differences between SFR- and mass-matched AGNs and inactive samples. While bars could potentially trigger AGN activity, they appear to have no further, unique effect on a galaxy’s stellar mass or SFR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".