A Machine Learning Approach to Classifying MESSENGER FIPS Proton Spectra. Issue 6 (23rd June 2020)

• Record Type: Journal Article
• Authors: James, Matthew K.; Imber, Suzanne M.; Raines, Jim M.; Yeoman, Timothy K.; Bunce, Emma J.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-23
• Subjects: MESSENGER -- FIPS Fast Imaging Plasma Spectrometer -- Mercury -- Plasma -- Neural Networks -- Density; Magnetospheric physics -- Periodicals; Space environment -- Periodicals; Cosmic physics -- Periodicals; Planets -- Atmospheres -- Periodicals; Heliosphere (Astrophysics) -- Periodicals; Geophysics -- Periodicals; 523.01
• DOI: 10.1029/2019JA027352
• Languages: English
• ISSNs: 2169-9380

Abstract

Abstract: The 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. Key Points: MESSENGER FIPS proton spectra are fitted with the κ distribution function Modular neural networks are used to assess quality of spectral fits ∼ 160, 000 "good" spectra are found near to and inside Mercury's magnetosphere