- Stony Brook University
- Center for Computational Astrophysics, Flatiron Institute
Searches for extrasolar planets have shown that planetary systems are not just common, but that they exhibit a broad and mostly unanticipated diversity of architectures. In this talk I will discuss how we can use machine learning methods to better characterize observed systems of close-in planets, and review what we have learnt about the formation and early evolution of planetary systems. Data on exoplanets, from observations of protoplanetary disks, and from the New Horizons mission to the outer Solar System, all motivate novel models in which planet formation proceeds far faster than was previously suspected. I will discuss the prospects for improved theoretical and observational tests of such models.
Join the Meeting: at this link (Zoom)