Poster: Unsupervised Learning for Extreme Space Weather Detection based on Spectrograms

Abstract

Approximately 2,000 satellites orbiting Earth relay telecommunications, broadcasting, and data communications to and from different locations globally. For example, in 2018,8.4 million households re-lied on satellite internet in the United States. However, extreme space weather, space environment phenomena driven by plasma be-tween stars and planets, can harm satellites and the global data and internet communications, and affect near-Earth space. Extracting features that represents those plasma waves requires highly-trained space scientists. We want to design machine learning (ML) meth-ods to automatically design features and extract information from plasma waves for the early detection of extreme space weather. To do that and to leverage the rich and state-of-the-art algorithms in computer vision, we first use Heliophysics Audified: Resonances in Plasmas (HARP) Sonification Data Processing package [1] to con-vert magnetospheric Ultra-Low Frequency (ULF) waves to sound and spectrograms. We then utilize unsupervised learning meth-ods to cluster plasma waves into different groups to capture the commonalities and differences between those activities. This initial and pilot exploration in the field offers the potential of practical applications of ML to space science field. The results will help with satellite-based internet and communications as part of the edge computing community.