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Register a new userA powerful machine learning technique to extract proton core, beam and alpha-particle parameters from velocity distribution functions in space plasmas
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Context: The analysis of the thermal part of velocity distribution functions (VDF) is fundamentally important for understanding the kinetic physics that governs the evolution and dynamics of space plasmas. However, calculating the proton core, beam and alpha-particle parameters for large data sets of VDFs is a time consuming and computationally demanding process that always requires supervision by a human expert. Aims: We developed a machine learning tool that can extract proton core, beam and alpha-particle parameters using images (2-D grid consisting pixel values) of VDFs. Methods: A database of synthetic VDFs is generated, which is used to train a convolutional neural network that infers bulk speed, thermal speed and density for all three particle populations. We generate a separate test data set of synthetic VDFs that we use to compare and quantify the predictive power of the neural network and a fitting algorithm. Results: The neural network achieves significantly smaller root-mean-square errors to infer proton core, beam and alpha-particle parameters than a traditional fitting algorithm. Conclusion: The developed machine learning tool has the potential to revolutionize the processing of particle measurements since it allows the computation of more accurate particle parameters than previously used fitting procedures.
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An innovative field-particle correlation technique is proposed that uses single-point measurements of the electromagnetic fields and particle velocity distribution functions to investigate the net transfer of energy from fields to particles associated with the collisionless damping of turbulent fluctuations in weakly collisional plasmas, such as the solar wind. In addition to providing a direct estimate of the local rate of energy transfer between fields and particles, it provides vital new information about the distribution of that energy transfer in velocity space. This velocity-space signature can potentially be used to identify the dominant collisionless mechanism responsible for the damping of turbulent fluctuations in the solar wind. The application of this novel field-particle correlation technique is illustrated using the simplified case of the Landau damping of Langmuir waves in an electrostatic 1D-1V Vlasov-Poisson plasma, showing that the procedure both estimates the local rate of energy transfer from the electrostatic field to the electrons and indicates the resonant nature of this interaction. Modifications of the technique to enable single-point spacecraft measurements of fields and particles to diagnose the collisionless damping of turbulent fluctuations in the solar wind are discussed, yielding a method with the potential to transform our ability to maximize the scientific return from current and upcoming spacecraft missions, such as the Magnetospheric Multiscale (MMS) and Solar Probe Plus missions.
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