  The Magnetospheric Multiscale Mission (MMS) seeks to study the micro-physics
of reconnection, which occurs at the magnetopause boundary layer between the
magnetosphere of Earth and the interplanetary magnetic field originating from
the sun. Identifying this region of space automatically will allow for
statistical analysis of reconnection events. The magnetopause region is
difficult to identify automatically using simple models, and time consuming for
scientists to classify by hand. We introduced a hierarchical Bayesian mixture
model with linear and auto regressive components to identify the magnetopause.
Using data from the MMS mission with the programming languages R and Stan, we
modeled and predicted possible regions and evaluated our performance against a
boosted regression tree model. Our model selects twice as many magnetopause
regions as the comparison model, without significant over selection, achieving
a 31\% true positive rate and 93\% true negative rate. Our method will allow
scientists to study the micro-physics of reconnection events in the
magnetopause using the large body of MMS data without manual classification.
