Co-hosted by the Department of Earth & Planetary Sciences.
This event is free and open to the public. No RSVP or ticket required.
University of Toronto's Centre for Planetary Sciences (CPS),
Canadian Institute for Theoretical Astrophysics (CITA)
A Million-fold Speedup in the Dynamical Characterization
of Multi-planet Systems
Many of the multi-planet systems discovered around other stars are dynamically packed to capacity. This implies that orbital integrations with masses or orbital parameters too far from the actual values will destabilize on short timescales; thus, long-term dynamics allows one to constrain the orbital architectures of many closely packed multi-planet systems. I will present a recent such application in the TRAPPIST-1 system, with 7 Earth-sized planets in the longest resonant chain discovered to date. In this case the complicated resonant phase space structure allows for strong constraints. A central challenge in such studies is the large computational cost of direct integrations, which preclude a full survey of the high-dimensional parameter space of orbital architectures allowed by observations. I will discuss our recent successes in training machine learning models capable of reliably predicting orbital stability a million times faster than direct integrations. This opens a wide discovery space for exoplanet characterization and planet formation studies as the next generation of spaceborne exoplanet surveys prepare for launch next year.