The properties of AGN in dwarf galaxies identified via SED fitting
Bibliographic record
Abstract
ABSTRACT Given their dominance of the galaxy number density, dwarf galaxies are central to our understanding of galaxy formation. While the incidence of active galactic nuclei (AGN) and their impact on galaxy evolution have been extensively studied in massive galaxies, much less is known about the role of AGN in the evolution of dwarfs. We search for radiatively efficient AGN in the nearby (0.1 < z < 0.3) dwarf (108 M⊙ < M⋆ < 1010 M⊙) population, using spectral energy distribution fitting (via prospector) applied to deep ultraviolet to mid-infrared photometry of 508 dwarf galaxies. Around a third (32 ± 2 per cent) of our dwarfs show signs of AGN activity. We compare the properties of our dwarf AGN to control samples, constructed from non-AGN, which have the same distributions of redshift and stellar mass as their AGN counterparts. Kolmogorov–Smirnov tests between the AGN and control distributions indicate that the AGN do not show differences in their distances to nodes, filaments, and nearby massive galaxies from their control counterparts. This indicates that AGN triggering in the dwarf regime is not strongly correlated with local environment. The fraction of AGN hosts with early-type morphology and those that are interacting are also indistinguishable from the controls within the uncertainties, suggesting that interactions do not play a significant role in inducing AGN activity in our sample. Finally, the star formation activity in dwarf AGN is only slightly lower than that in their control counterparts, suggesting that the presence of radiatively efficient AGN does not lead to significant, prompt quenching of star formation in these systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".