  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.
