Abstract:

Thanks to the massive successes of Kepler, K2, and now TESS, we have more asteroseismic data to work with than ever before. With these large catalogues come fun new ways of leveraging large quantities of data to make inferences of stellar physics. One of these methods is the Bayesian Hierarchical Model, which allows you to create more flexible models that effectively incorporate sources of uncertainty.

In this seminar, I’ll give you an introduction to Bayesian statistics with a focus on hierarchical models, and examples of how this has been applied to asteroseismic populations. In the second half, I’ll present our latest results measuring precise asteroseismic rotation rates from Kepler data, and using these rates to constrain the presence of weakened magnetic braking in the evolution of Sun-like main sequence stars, both using Bayesian statistics and hierarchical models