PLANET POPULATION SYNTHESIS
W. Benz (University of Bern, Center for Space and Habitability, Bern, Switzerland),
S. Ida (Tokyo University of Technology, Earth and Planetary Science, Japan),
Y. Alibert (University of Bern, Physikalisches Institut, Center for Space and Habitability, Switzerland),
D. Lin (University of Santa Cruz, Astronomy, United States),
C. Mordasini (MPIA, Germany)
The common existence of exoplanets and their rich structural and dynamical diversity indicates that
their formation is a robust process, their evolution is driven by many competing physical processes
and their destiny is determined by a wide range of boundary conditions. Population synthesis models
provide a useful tool for the interpretation of exoplanets' present-day properties and the understanding
of the dominant effects at each stages of their evolutionary paths. They also generate testable predictions
which can be used to guide future observational strategy and model upgrades. The construction
of comprehensive models and the exploration of the vast parameter space is still in a developmental
stage. We review here the present status of this approach including 1) an exhaustive account of potentially
relevant physical processes, 2) a summary of the quantitative prescriptions which are introduced
to approximate the consequence of each effect, 3) a report on various computational methods to simulate
statistical distributions, and 4) a compilation of the assumed boundary and initial conditions. We
outline some outstanding theoretical uncertainties and accentuate the need of some critical observational
calibrations. We confront the results of some simulated planetary census with existing data and
present both the successful reproduction and disagreements between current versions of population
synthesis models and observations. We identify which results can be considered reliable and which
are still based on unknown processes and/or parameters. Finally, we suggest future theoretical and
observational investigations that can improve the reliability of the models.
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