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Innovative technique for separating proton core, proton beam, and alpha particles in solar wind 3D velocity distribution functions

De Marco, R; Bruno, R; Jagarlamudi, VK; D'amicis, R; Marcucci, MF; Fortunato, V; Perrone, D; ... Horbury, T; + view all (2023) Innovative technique for separating proton core, proton beam, and alpha particles in solar wind 3D velocity distribution functions. Astronomy and Astrophysics , 669 , Article A108. 10.1051/0004-6361/202243719. Green open access

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Abstract

Context. The identification of proton core, proton beam, and alpha particles in solar wind ion measurements is usually performed by applying specific fitting procedures to the particle energy spectra. In many cases, this turns out to be a challenging task due to the overlapping of the curves. Aims. We propose an alternative approach based on the statistical technique of clustering, a standard tool in many data-driven and machine learning applications. Methods. We developed a procedure that adapts clustering to the analysis of solar wind distribution functions. We first tested the method on a synthetic data set and then applied it to a time series of solar wind data. Results. The moments obtained for the different particle populations are in good agreement with the official data set and with the statistical studies available in the literature. Conclusions. Our method is shown to be a very promising technique that can be combined with the traditional fitting algorithms in working out difficult cases that involve the identification of particle species in solar wind measurements.

Type: Article
Title: Innovative technique for separating proton core, proton beam, and alpha particles in solar wind 3D velocity distribution functions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1051/0004-6361/202243719
Publisher version: https://doi.org/10.1051/0004-6361/202243719
Language: English
Additional information: © The Authors 2023. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: solar wind, plasmas, methods: statistical, instabilities, methods: data analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics
URI: https://discovery.ucl.ac.uk/id/eprint/10167697
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