Orateur
Description
How black holes accrete to super-massive monsters in centres of nuclear galaxies and in this luminous process impact galaxies is a key open question in galaxy evolution. The puzzle has remained in many pieces: Selection effects confuse, and most studies focus on two or three parameters. Yet, these indicate a multi-variate, probabilistic link between AGN accretion, obscuration, and the host galaxy's mass, star-formation, and morphology. A new window to cleanly select statistical samples to investigate these inter-dependencies opened with eROSITA. We aim to develop a scalable analysis of the X-ray to IR SED for millions of sources, to decode which host galaxies preferentially undergo a quasar phase and experience its energy release.
Starting from the known evolving galaxy population, we trigger AGN with parametric probabilistic distributions and generate X-ray survey samples. To generate galaxy-AGN fluxes with realistic errors, we use the new GRAHSP SED fitting engine, validated to retrieve host galaxy mass even in bright AGN. To feasibly compute survey likelihoods we introduce a new machine-learning-based survey likelihood, supported by an explicit generative model and advances in deep neural network models. The likelihood allows building extensible models. In our application, we explore the mass and star-formation-dependence of AGN triggering, jointly with the geometry of the AGN accretion engine.
