Galaxy formation and evolution

Abstract:

Abstract We are standing at the crossroads of powerful new facilities emerging in the next decade on the ground and in space like ELT, SKA, JWST, and Athena. Turning the narrative of the star formation potential of galaxies into a quantitative theory will provide answers to many outstanding questions in astrophysics, from the formation of planets to the evolution of galaxies and the origin of heavy elements. To achieve this goal, there is an urgent need for a dedicated space-borne, far-infrared spectroscopic facility capable of delivering, for the first time, large scale, high spectral resolution (velocity resolved) multiwavelength studies of the chemistry and dynamics of the ISM of our own Milky Way and nearby galaxies. The Far Infrared Spectroscopic Surveyor (FIRSS) fulfills these requirements and by exploiting the legacy of recent photometric surveys it seizes the opportunity to shed light on the fundamental building processes of our Universe.

Abstract:

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.