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After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

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., Comment: Accepted in Astronomy and Astrophysics