Dynamical chaos as a tool for characterizing multi-planet systems
APA
Tamayo, D. (2017). Dynamical chaos as a tool for characterizing multi-planet systems. Perimeter Institute. https://pirsa.org/17090064
MLA
Tamayo, Dan. Dynamical chaos as a tool for characterizing multi-planet systems. Perimeter Institute, Sep. 19, 2017, https://pirsa.org/17090064
BibTex
@misc{ pirsa_PIRSA:17090064, doi = {10.48660/17090064}, url = {https://pirsa.org/17090064}, author = {Tamayo, Dan}, keywords = {Cosmology}, language = {en}, title = {Dynamical chaos as a tool for characterizing multi-planet systems}, publisher = {Perimeter Institute}, year = {2017}, month = {sep}, note = {PIRSA:17090064 see, \url{https://pirsa.org}} }
Many of the multi-planet systems discovered around other stars are maximally packed. This implies that simulations 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 N-body simulations, 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 predicting orbital stability a million times faster than N-body simulations, and the discovery space that this opens up for exoplanet characterization and planet formation studies.