A powerful machine learning technique to extract proton core, beam, and α-particle parameters from velocity distribution functions in space plasmas

Context. The analysis of the thermal part of velocity distribution functions (VDFs) 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 (2D grid consisting pixel values) of VDFs. Methods. A database of synthetic VDFs was generated, which was used to train a convolutional neural network that infers bulk speed, thermal speed, and density for all three particle populations. We generated a separate test data set of synthetic VDFs that we used 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. Conclusions. 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.

Automated summary

The study developed a machine learning tool that can extract proton core, beam, and alpha-particle parameters using images of VDFs. A database of synthetic VDFs was generated to train a convolutional neural network to infer particle populations' speed and density, achieving smaller errors compared to traditional fitting algorithms. The developed tool has the potential to revolutionize particle measurements processing by computing more accurate parameters.