A novel machine learning technique to identify and categorize plasma waves in spacecraft measurements

TL;DR

This paper introduces a machine learning method using Self-Organizing Maps to efficiently identify and categorize plasma waves in large spacecraft magnetic field data sets, significantly reducing manual effort.

Contribution

The paper presents a novel application of Self-Organizing Maps for rapid plasma wave identification in large-scale space physics data sets.

Findings

Effective data reduction and grouping of magnetic spectra

Significantly reduces manual data inspection

Accelerates discovery of plasma wave forms

Abstract

The available magnetic field data from the terrestrial magnetosphere, solar wind and planetary magnetospheres exceeds over $10^6$ hours. Identifying plasma waves in these large data sets is a time consuming and tedious process. In this Paper, we propose a solution to this problem. We demonstrate how Self-Organizing Maps can be used for rapid data reduction and identification of plasma waves in large data sets. We use 72,000 fluxgate and 110,000 search coil magnetic field power spectra from the Magnetospheric Multiscale Mission (MMS$_1$) and show how the Self-Organizing Map sorts the power spectra into groups based on their shape. Organizing the data in this way makes it very straightforward to identify power spectra with similar properties and therefore this technique greatly reduces the need for manual inspection of the data. We suggest that Self-Organizing Maps offer a time effective and robust technique, which can significantly accelerate the processing of magnetic field data and discovery of new wave forms.