Large-Scale Statistical Survey of Magnetopause Reconnection

TL;DR

This paper presents a hierarchical Bayesian model to automatically identify magnetopause regions in MMS data, enabling large-scale statistical analysis of reconnection events with improved accuracy over previous methods.

Contribution

Introduces a hierarchical Bayesian mixture model for automatic magnetopause detection, outperforming boosted regression trees in identifying relevant regions in MMS data.

Findings

Model selects twice as many magnetopause regions as comparison.

Achieves 31% true positive rate and 93% true negative rate.

Enables large-scale, automated analysis of reconnection events.

Abstract

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.