Proton-proton collisional age to order solar wind types

Heidrich-Meisner, Verena; Berger, L.; Wimmer‐Schweingruber, R. F.

doi:10.1051/0004-6361/201937378

Cited by 3 publications

(3 citation statements)

References 32 publications

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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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“…• -equivalent proton-proton collisions in the plasma as an additional parameter. As shown in Kasper et al (2012);Heidrich-Meisner et al (2020), the proton-proton collisional age (in the following also referred to as collisional age) is a suitable ordering parameter that summarizes the collisional transport history of the solar wind. We compute the collisional age in the same way as in Heidrich-Meisner et al (2020):…”

Section: Ace Datamentioning

confidence: 99%

“…The collisional age depends (non-linearly) on the proton density, proton temperature and proton speed. If only a single input parameter is supplied to kmeans, the proton-proton collisional age is the best choice (as also discussed in Heidrich-Meisner et al (2020)). This clearly shows the benefit of using informative, physical, non-linear parameters for solar wind classification.…”

Section: Implications For the Non-linear Parametersmentioning

confidence: 99%

Influence of solar wind parameters on unsupervised solar wind classification with k-means

Teichmann

Heidrich-Meisner

Berger

et al. 2023

Preprint

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The properties of the solar wind represent a mixture of indicators for solar origin and transport effects. Both are of interest for the understanding of heliophysics and space weather effects. Most available solar wind classifications focus on the solar origin, in part based on transport effected properties. We aim to identify the solar wind properties that are most important for solar wind classification. We select seven solar wind properties: proton density, proton speed, proton temperature, absolute magnetic field strength, proton-proton collisional age, the ratio between the densities of O6+ and O7+ and the mean charge state of Fe. We apply an unsupervised machine learning method, k-means, to each subset of the these parameters and compare the results to a reference case based on all seven solar wind properties. Two scenarios are considered which provide a simple and a detailed solar wind classification, respectively. We identified the proton density as the most important solar wind property for solar wind classification. Furthermore, we found that charge state composition is important to accurately identify the solar source region. This holds for the simple case of three solar wind types but is even more important for a more detailed classification. In comparison to proton density and proton temperature, the solar wind speed turns out to be a less influential property. Our results underscore the importance of highly accurate measurements, in particular for proton density, proton temperature and the charge state composition.

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Scope and limitations of ad hoc neural network reconstructions of solar wind parameters

Hecht¹,

Heidrich-Meisner²,

Berger³

et al. 2023

A&A

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Context. Solar wind properties are determined by the conditions of their solar source region and transport history. Solar wind parameters, such as proton speed, proton density, proton temperature, magnetic field strength, and the charge state composition of oxygen, are used as proxies to investigate the solar source region of the solar wind. The solar source region of the solar wind is relevant to both the interaction of this latter with the Earth’s magnetosphere and to our understanding of the underlying plasma processes, but the effect of the transport history of the wind is also important. The transport and conditions in the solar source region affect several solar wind parameters simultaneously. Therefore, the typically considered solar wind properties (e.g., proton density and oxygen charge-state composition) carry redundant information. Here, we are interested in exploring this redundancy. Aims. The observed redundancy could be caused by a set of hidden variables that determine the solar wind properties. We test this assumption by determining how well a (arbitrary, non-linear) function of four of the selected solar wind parameters can model the fifth solar wind parameter. If such a function provided a perfect model, then this solar wind parameter would be uniquely determined from hidden variables of the other four parameters and would therefore be redundant. If no reconstruction were possible, this parameter would be likely to contain information unique to the parameters evaluated here. In addition, isolating redundant or unique information contained in these properties guides requirements for in situ measurements and development of computer models. Sufficiently accurate measurements are necessary to understand the solar wind and its origin, to meaningfully classify solar wind types, and to predict space weather effects. Methods. We employed a neural network as a function approximator to model unknown, arbitrary, non-linear relations between the considered solar wind parameters. This approach is not designed to reconstruct the temporal structure of the observations. Instead a time-stable model is assumed and each point of measurement is treated separately. This approach is applied to solar wind data from the Advanced Composition Explorer (ACE). The neural network reconstructions are evaluated in comparison to observations, and the resulting reconstruction accuracies for each reconstructed solar wind parameter are compared while differentiating between different solar wind conditions (i.e., different solar wind types) and between different phases in the solar activity cycle. Therein, solar wind types are identified according to two solar-wind classification schemes based on proton plasma properties. Results. Within the limits defined by the measurement uncertainties, the proton density and proton temperature can be reconstructed well. Each parameter was evaluated with multiple criteria. Overall proton speed was the parameter with the most accurate reconstruction, while the oxygen charge-state ratio and magnetic field strength were most difficult to recover. We also analysed the results for different solar wind types separately and found that the reconstruction is most difficult for solar wind streams preceding and following stream interfaces. Conclusions. For all considered solar wind parameters, but in particular the proton density, proton temperature, and the oxygen charge-state ratio, parameter reconstruction is hindered by measurement uncertainties. The proton speed, while being one of the easiest to measure, also seems to carry the highest degree of redundancy with the combination of the four other solar wind parameters. Nevertheless, the reconstruction accuracy for the proton speed is limited by the large measurement uncertainties on the respective input parameters. The reconstruction accuracy of sector reversal plasma is noticeably lower than that of streamer belt or coronal hole plasma. We suspect that this is a result of the effect of stream interaction regions, which strongly influence the proton plasma properties and are typically assigned to sector reversal plasma. The fact that the oxygen charge-state ratio –a non-transport-affected property– is difficult to reconstruct may imply that recovering source-specific information from the transport-affected proton plasma properties is challenging. This underlines the importance of measuring the heavy ion charge-state composition.

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Proton-proton collisional age to order solar wind types

Cited by 3 publications

References 32 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

Influence of solar wind parameters on unsupervised solar wind classification with k-means

Scope and limitations of ad hoc neural network reconstructions of solar wind parameters

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