Visualizing and Interpreting Unsupervised Solar Wind Classifications

Amaya, Jorge; Dupuis, Romain; Innocenti, Maria Elena; Lapenta, Giovanni

doi:10.3389/fspas.2020.553207

Cited by 14 publications

(26 citation statements)

References 59 publications

(108 reference statements)

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“…Neugebauer et al, 2003(cf. Neugebauer et al, , 2016Reisenfeld et al, 2003;Zhao et al, 2009;Xu and Borovsky, 2015;Camporeale et al, 2017;Veselovsky et al, 2018;Li et al, 2020;Amaya et al, 2020;Heidrich-Meisner et al, 2020;Bloch et al, 2020). One characteristic difference between the various types of plasma at 1 AU is the intensity of the electron strahl (Borovsky, 2018).…”

Section: The Electron Strahl and The Types Of Solar Wind Plasmamentioning

confidence: 99%

Exploring the Properties of the Electron Strahl at 1 AU as an Indicator of the Quality of the Magnetic Connection Between the Earth and the Sun

Borovsky

2021

Front. Astron. Space Sci.

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In this report some properties of the electron strahl at 1 AU are examined to assess the strahl at 272 eV as an indicator of the quality of the magnetic connection of the near-Earth solar wind to the Sun. The absence of a strahl has been taken to represent either a lack of magnetic connection to the corona or the strahl not surviving to 1 AU owing to scattering. Solar-energetic-electron (SEE) events can be used as indicators of good magnetic connection: examination of 216 impulsive SEE events finds that they are all characterized by strong strahls. The strahl intensity at 1 AU is statistically examined for various types of solar-wind plasma: it is found that the strahl is characteristically weak in sector-reversal-region plasma. In sector-reversal-region plasma and other slow wind, temporal changes in the strahl intensity at 1 AU are examined with 64 s resolution measurements and the statistical relationships of strahl changes to simultaneous plasma-property changes are established. The strahl-intensity changes are co-located with current sheets (directional discontinuities) with strong changes in the magnetic-field direction. The strahl-intensity changes at 1 AU are positively correlated with changes in the proton specific entropy, the proton temperature, and the magnetic-field strength; the strahl-intensity changes are anti-correlated with changes in the proton number density, the angle of the magnetic field with respect to the Parker-spiral direction, and the alpha-to-proton number-density ratio. Reductions in the strahl intensity are not consistent with expectations for a simple model of whistler-turbulence scattering. Reductions in the strahl intensity are mildly consistent with expectations for Coulomb scattering, however the strongest-observed plasma-change correlations are unrelated to Coulomb scattering and whistler scattering. The implications of the strahl-intensity-change analysis are that the change in the magnetic-field direction at a strahl change represents a change in the magnetic connection to the corona, resulting in a different strahl intensity and different plasma properties. An outstanding question is: Does an absence of an electron strahl represent a magnetic disconnection from the Sun or a poor strahl source in some region of the corona?

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Section: The Electron Strahl and The Types Of Solar Wind Plasmamentioning

confidence: 99%

Exploring the Properties of the Electron Strahl at 1 AU as an Indicator of the Quality of the Magnetic Connection Between the Earth and the Sun

Borovsky

2021

Front. Astron. Space Sci.

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“…OpenGGCM-CTIM-RCM is available at the Community Coordinated Modeling Center at NASA Goddard Space Flight Center (GSFC) for model runs on demand. A detailed description of the model and typical examples of OpenGGCM applications can be found in Raeder (2003), Raeder et al (2001b), Raeder and Lu (2005), Connor et al (2016), Raeder et al (2001a), Ge et al (2011), Raeder et al (2010, Ferdousi and Raeder (2016), Dorelli (2004), Raeder (2006), Berchem et al (1995), Moretto et al (2006), Vennerstrom et al (2005), Anderson et al (2017), Zhu et al (2009), Zhou et al (2012), andShi et al (2014), to name a few. Of particular relevance to this study is OpenGGCM-CTIM-RCM simulations that have recently been used for a domain of influence analysis, a technique rooted in data assimilation that can be used to understand what the most promising locations are for monitoring (i.e., spacecraft placing) in a complex system such as the magnetosphere (Millas et al, 2020).…”

Section: Global Magnetospheric Simulationsmentioning

confidence: 99%

“…3 Self-organizing maps: a recap To classify magnetospheric regions, we use self-organizing maps (SOMs), an unsupervised ML technique. Selforganizing maps (Kohonen, 1982;Villmann and Claussen, 2006;Kohonen, 2014;Amaya et al, 2020), also known as Kohonen maps or self-organizing feature maps, are a clustering technique based on a neural network architecture. SOMs aim at producing an ordered representation of data, which in most cases has lower dimensionality with respect to the data itself.…”

Section: Global Magnetospheric Simulationsmentioning

confidence: 99%

Unsupervised classification of simulated magnetospheric regions

et al. 2021

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Abstract. In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

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“…A more recent version of the SOM that does not depend on the iteration number has been proposed by Rougier and Boniface (2011a). This Dynamic Self-Organizing Map (DSOM) has been succesfully used by Amaya et al (2020) to classify different solar wind types. In this work, we have decided to use the original SOM algorithm as the results already show very good convergence to meaningful classes.…”

Section: On the Choice Of Training Featuresmentioning

confidence: 99%

“…Maps (Kohonen, 1982;Villmann and Claussen, 2006;Amaya et al, 2020), also known as Kohonen maps or self organizing feature maps, are a clustering technique based on a neural network architecture. A SOM is composed mainly of two parts: a (usually) two dimensional lattice of Lr × Lc = q nodes, with Lr and Lc the number of rows and columns.…”

mentioning

confidence: 99%

Unsupervised classification of simulated magnetospheric regions

Innocenti

Amaya

Raeder

et al. 2021

Preprint

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Abstract. In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or opera- tional interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

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Visualizing and Interpreting Unsupervised Solar Wind Classifications

Cited by 14 publications

References 59 publications

Exploring the Properties of the Electron Strahl at 1 AU as an Indicator of the Quality of the Magnetic Connection Between the Earth and the Sun

Exploring the Properties of the Electron Strahl at 1 AU as an Indicator of the Quality of the Magnetic Connection Between the Earth and the Sun

Unsupervised classification of simulated magnetospheric regions

Unsupervised classification of simulated magnetospheric regions

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