# Automated classification of plasma regions using 3D particle energy distributions

arXiv: https://arxiv.org/abs/1908.05715 · HTML: https://mlsanity.com/paper/1908.05715/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload](https://mlsanity.com/paper/2406.14297/) (2024)
- [Unsupervised classification of simulated magnetospheric regions](https://mlsanity.com/paper/2109.04916/) (2021)
- [Unsupervised classification of fully kinetic simulations of plasmoid instability using Self-Organizing Maps (SOMs)](https://mlsanity.com/paper/2304.13469/) (2023)
- [Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions](https://mlsanity.com/paper/2504.15846/) (2025)
- [Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference](https://mlsanity.com/paper/2603.14091/) (2026)
- [Automated Classification of Plasma Regions at Mars Using Machine Learning](https://mlsanity.com/paper/2604.17131/) (2026)

## Abstract (arXiv)

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the MMS on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is >98%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.