Characterization of Relativistic Electron Precipitation Events Observed by the CALET Experiment Using Self-Organizing-Maps

Relativistic electron precipitation (REP) is a relatively high-latitude phenomenon where high-energy electrons trapped in the outer radiation belt are lost into the Earth's atmosphere. REP events observed at low Earth orbit show varying temporal profiles and global distributions. While the precipitation origin has been attributed to specific wave modes or scattering sources, the sorting of REP events by type or driver remains an unsolved challenge. In this study, we analyze the temporal profile of relativistic electron precipitation events observed by the CALorimetric Electron Telescope (CALET) experiment on board the International Space Station. We use an unsupervised machine learning technique called Self-Organizing-Maps (SOM) to automatically detect and then classify relativistic electron events observed by the two scintillator layers at the top of the apparatus, sensitive to electrons with energies >1.5 MeV and >3.4 MeV, respectively. We calculate the power spectral density (PSD) of the count rates observed by both sensors and use them as an input for the SOM. The SOM technique groups the PSDs by their similarity, resulting in a classification of relativistic electron events by the periodicity of the observed precipitation. We investigate the L-shell and magnetic local time distribution of the resulting classification, and energy spectral index associated with the observations. Clear precipitation patterns are observed and compared to past precipitation categorization attempts as well as known distributions of various scattering mechanisms. The classification reveals features through the sorting of the variability of the rapid precipitation, allowing the identification of different precipitation populations with varying properties. Plain Language Summary Fast electrons are normally trapped by the Earth's magnetic field. However, they often get released in bursts and impact the upper layers of the atmosphere near the poles. The underlying processes are still not well understood and debated. In this study we use an unsupervised artificial intelligence technique called Self-Organizing-Maps (SOM) to automatically detect and classify the observations made by a charged particle detector onboard the International Space Station (ISS). The SOM categorizes the bursts based on their variability and group together observations by their similarity. We compare the categorization with the spatial location of the electron bursts. Clear patterns are observed and compared with past categorizations attempts.