Large-Scale Statistical Survey of Magnetopause Reconnection
This enables scientists to study reconnection micro-physics using large-scale MMS data without manual classification, but it is incremental as it improves on existing methods for a specific domain.
The paper tackled the problem of automatically identifying the magnetopause region for statistical analysis of reconnection events, achieving a 31% true positive rate and 93% true negative rate, which is twice as many regions as a comparison model without over-selection.
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.