Classification of Space Particle Events using Supervised Machine Learning Algorithms

Abstract

Solar Particle Events (SPEs) generate cosmic radiation of different magnitude in a time span of several hours or even days. This contributes to an increased probability of higher magnitude Single-Event Upsets (SEUs) occurrence in space applications. It is critical to establish early detection of SEU rate or Soft Error Rate (SRE) changes to enable timely radiation hardening measures. This research paper focuses on the high-accuracy detection of SPEs using the manually collected space data. Additionally, the prediction of SRE increase or decrease was established with the seven widely used supervised machine learning algorithms. Excellent performance of 97.82%, including a high F1-score, was achieved during the presence of SPE using $k$-Nearest Neighbor algorithms.