A machine learning approach to classifying MESSENGER FIPS proton spectra

The kappa 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 kappa distribution function is fitted to each proton spectrum using the downhill-simplex method, providing an estimate for density,n, temperature,T, and the kappa 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 similar to 4 years in orbit of Mercury. Of the 223,282 spectra, similar to 160,000 were classified as having "good" fitting kappa distributions, similar to 133,000 of which were measurements obtained from within the magnetosphere, and similar to 18,000 were from the magnetosheath.