A Machine Learning Approach to Classifying MESSENGER FIPS Proton Spectra

Abstract

AbstractThe κ distribution function is fitted to the entire data set of MErcury Surface, Space ENvironment, GEochemistry and Ranging's (MESSENGER) 1‐min Fast Imaging Plasma Spectrometer (FIPS Andrews et al., 2007, https://doi.org/10.1007/s11214‐007‐9272‐5) proton spectra, and then artificial neural networks (ANNs) are used to assess the quality of this fit to the data. The κ distribution function is fitted to each proton spectrum using the downhill‐simplex method, providing an estimate for density, n, temperature, T, and the κ parameter, which controls the shape of the distribution. The final trained neural network achieved classification accuracy of 96% and has been used to classify the 1‐min proton data set collected during MESSENGER's ∼4 years in orbit of Mercury. Of the 223,282 spectra, ∼160,000 were classified as having “good” fitting κ distributions, ∼133,000 of which were measurements obtained from within the magnetosphere, and ∼18,000 were from the magnetosheath.