The co-evolution of strong AGN and central galaxies in different environments
Authors: V. M. Sampaio, A. Aragón-Salamanca, M. R. Merrifield, R. R. de Carvalho, S. Zhou, I. Ferreras
Abstract: We exploit a sample of 80,000 SDSS central galaxies to investigate the effect of AGN feedback on their evolution. We trace the demographics of optically-selected AGN (Seyferts) as a function of their internal properties and environment. We find that the preeminence of AGN as the dominant ionising mechanism increases with stellar mass, overtaking star-formation for galaxies with $M_\text{stellar} \geq 10^{11}M_\odot$. The AGN fraction changes systematically with the galaxies' star-formation activity. Within the blue cloud, this fraction increases as star-formation activity declines, reaching a maximum near the green valley ($\sim 17 \pm 4\%$), followed by a decrease as the galaxies transition into the red sequence. This systematic trend provides evidence that AGN feedback plays a key role in regulating and suppressing star formation. In general, Seyfert central galaxies achieve an early-type morphology while they still host residual star formation. This suggests that, in all environments, the morphology of Seyfert galaxies evolves from late- to early-type before their star formation is fully quenched. Stellar mass plays an important role in this morphological transformation: while low mass systems tend to emerge from the green valley with an elliptical morphology (T-Type $\sim -2.5 \pm 0.7$), their high-mass counterparts maintain a spiral morphology deeper into the red sequence. In high-stellar-mass centrals, the fraction of Seyferts increases from early- to late-type galaxies, indicating that AGN feedback may be linked with the morphology and its transformation. Our analysis further suggests that AGN are fuelled by their own host halo gas reservoir, but when in group centrals can also increase their gas reservoir via interactions with satellite galaxies.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.