Feedback and Outflows: The effects of AGN on their host galaxies
Is AGN feedback actually occurring and playing any relevant role in shaping galaxies and their scaling relations with black holes?
WP2 aims at isolating candidate AGN in the feedback phase via convolutional neural networks applied to both imaging and spectroscopy within the optically/IR and optically-faint/X-ray AGN phases.
Task 2A: Probing indirect signatures of AGN feedback
We will adopt deep learning to explore the multi-parameter space of outflow signatures, star-formation rate, morphology, gas content of AGN selected in multiwavelength X-ray fields (e.g., COSMOS, CDFs, XXL) to reveal the fundamental trends among these parameters. We will complement this approach using cutting-edge Bayesian inference and Monte Carlo techniques to account for selection effects.
Task 2B: Shedding light on AGN quenching
We will explore AGN feedback at low and intermediate redshifts (up to z~1-3), using dedicated XMM-Newton observations (to sample winds at the accretion disc scale; panels a & d in diagram above) and Integral Field Unit (IFU) of X-ray AGN in optical-mm regime (to sample the winds at the galaxy scale; panels b/c/e/f in diagram above). We will also use accurate NIR spectroscopy of local AGN samples (e.g., EMIR) to study the properties and demography of AGN outflows and characterize their host stellar populations.