Statistical and Dynamical Remastering of Classic Exoplanet Systems

Open Access
Nelson, Benjamin E
Graduate Program:
Astronomy and Astrophysics
Doctor of Philosophy
Document Type:
Date of Defense:
May 20, 2015
Committee Members:
  • Eric Ford, Dissertation Advisor
  • Eric Ford, Committee Chair
  • Jason Wright, Committee Member
  • Steinn Sigurdsson, Committee Member
  • Ronald Lynn Gilliland, Committee Member
  • G Jogesh Babu, Committee Member
  • exoplanets
  • planetary dynamics
  • radial velocity
  • Bayesian statistics
  • astrophysics
The most powerful constraints on planet formation will come from characterizing the dynamical state of multi-planet systems. Unfortunately, with that complexity comes a number of factors that make analyzing these systems a computationally challenging endeavor: the sheer number of model parameters, a wonky shaped posterior distribution, and hundreds to thousands of time series measurements. For my dissertation, I will review our efforts to improve the statistical analyses of radial velocity (RV) data and their applications to some renown, dynamically complex exoplanet systems. In the first project, we develop a differential evolution Markov chain Monte Carlo algorithm (RUN DMC) to tackle the aforementioned difficult aspects of data analysis. We test the robustness of the algorithm in regards to the number of modeled planets (model dimensionality) and increasing dynamical strength. Next, we apply RUN DMC to two classic multi-planet systems (55 Cancri and Gliese 876) and one highly debated multi-body system (nu Octantis) from radial velocity surveys. We report our key results regarding the coplanarity and resonant nature of these systems. These empirically derived models motivate the need for more sophisticated algorithms to analyze exoplanet data and will provide new challenges for planet formation models.