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

TL;DR

This paper introduces a machine learning method using convolutional neural networks to efficiently and accurately extract proton core, beam, and alpha-particle parameters from velocity distribution functions in space plasmas, outperforming traditional fitting methods.

Contribution

A novel machine learning tool employing CNNs to automatically extract particle parameters from VDF images, reducing computation time and increasing accuracy compared to traditional methods.

Findings

Neural network achieves lower root-mean-square errors than fitting algorithms.

The method can process large datasets more efficiently.

It provides more accurate particle parameters for space plasma analysis.

Abstract

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.