A machine learning approach to classifying MESSENGER FIPS proton spectra

Abstract

The entire dataset of 1-minute proton spectra provided by MESSENGER’s Fast Imaging Plasma Spectrometer (FIPS [1]) are each numerically fitted with a κ distribution function in order to provide estimates of proton density, n, and temperature, T , in the vicinity of Mercury’s magnetosphere. A collection of artificial neural networks (ANNs) are trained to assess the quality of the newly fitted distribution functions, with a final classification accuracy of 96%. 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. The “good” magnetospheric densities and temperatures were subsequently used to create a 3D ANN-based model of the Hermean magnetosphere.

1. Introduction

Mass loading is an important factor in controlling the timescales over which processes such as wave propagation and reconnection can occur within a magnetized plasma. The speed at which magnetohydrodynamic (MHD) waves propagate is characterized by the local Alfvén velocity, v_A , where v_A= , B is themagnetic field magnitude, ρ is the plasma mass density and μ₀ is the permeability of free space. Fundamentally, the higher the local plasma mass density is–the slower waves propagate. Similarly, the beta of a plasma (β) depends on the density and temperature where β= and k_B is the Boltzmann constant.

Magnetic reconnection between two adjacent magnetized plasmas is affected by the difference in plasma beta (∆β) between the two fluids, where a larger ∆β suppresses reconnection and thus a larger shear angle is required for reconnection to occur [2, 3]; conversely, low ∆β makes reconnection more favorable with small shear angles [3, 4].
The Fast Imaging Plasma Spectrometer (FIPS [1]) on board the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER [5]) spacecraft operated at Mercury for approximately 4 years (from March 2011 to April 2015) during the orbital phase of the mission. During this time, it sampled the ions from both within and outside of the Hermean magnetosphere, providing at least 1.25 million proton spectra every 10 or 60 s, depending upon the instrument mode. Many of those were burst-mode spectra and were combined in groups of 6 to form 1-minute average spectra, resulting in ∼220,000 60 s proton spectra.

In the work presented here, the entire dataset of 1-minute FIPS spectra are each numerically fitted with κ distribution functions, providing improved estimates of proton density and temperature over previous Maxwellian fits. The quality of the distribution function fits are assessed using artificial neural networks, resulting in a reduced dataset of more reliable plasma moments. Finally, new average and scalable 3D models of magnetospheric protons are presented using the new moments.

2. Fitting the Spectra

The κ-distribution function, f_κ [6],
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