Solar wind data analysis aided by synthetic modeling: A better understanding of plasma frame variations from temporal data Article Swipe
Norbert Magyar
,
J. L. Verniero
,
Á. Szabó
,
Jinfeng Zhang
,
Tom Van Doorsselaere
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/0004-6361/202348071
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/0004-6361/202348071
Context. In situ measurements of the solar wind, a turbulent and anisotropic plasma flow originating at the Sun, are mostly carried out by single spacecraft, resulting in one-dimensional time series. Aims. The conversion of these measurements to the spatial frame of the plasma is a great challenge, but it is required for direct comparison of the measurements with magnetohydrodynamic turbulence theories. Methods. We present a tool kit based on the synthetic modeling of solar wind fluctuations as two-dimensional noise maps with adjustable spectral and power anisotropy that can help with the temporal-spatial conversion of real data. Specifically, by following the spacecraft trajectory through a noise map (relative velocity and angle relative to some mean magnetic field) with properties tuned to mimic those of the solar wind, the likelihood that the temporal data fluctuations represent parallel or perpendicular fluctuations in the plasma frame can be quantified by correlating structure functions of the noise map. Synthetic temporal data can also be generated, which can provide a testing ground for analysis applied to the solar wind data. Results. We demonstrate this tool by investigating Parker Solar Probe’s seventh encounter trajectory and data, and we showcase several possible ways in which it can be used. We find that whether temporal variations in the spacecraft frame come from parallel or perpendicular variations in the plasma frame strongly depends on the spectral and power anisotropy of the measured wind. Conclusions. Data analysis assisted by such underlying synthetic models as presented here could open up new ways to interpret measurements in the future, specifically in the more reliable determination of plasma frame quantities from temporal measurements.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1051/0004-6361/202348071
- OA Status
- hybrid
- References
- 42
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4399304637Canonical identifier for this work in OpenAlex
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https://doi.org/10.1051/0004-6361/202348071Digital Object Identifier
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Solar wind data analysis aided by synthetic modeling: A better understanding of plasma frame variations from temporal dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-03Full publication date if available
- Authors
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Norbert Magyar, J. L. Verniero, Á. Szabó, Jinfeng Zhang, Tom Van DoorsselaereList of authors in order
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https://doi.org/10.1051/0004-6361/202348071Publisher landing page
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://doi.org/10.1051/0004-6361/202348071Direct OA link when available
- Concepts
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Physics, Astrophysics, Plasma, Data analysis, Astronomy, Solar wind, Frame (networking), Solar physics, Solar atmosphere, Data mining, Nuclear physics, Telecommunications, Magnetic field, Quantum mechanics, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.power | 84, 227 |
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| abstract_inverted_index.adjustable | 81 |
| abstract_inverted_index.anisotropy | 85, 228 |
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| abstract_inverted_index.quantities | 265 |
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| abstract_inverted_index.trajectory | 101, 186 |
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| abstract_inverted_index.underlying | 239 |
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| abstract_inverted_index.investigating | 180 |
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| abstract_inverted_index.two-dimensional | 77 |
| abstract_inverted_index.temporal-spatial | 91 |
| abstract_inverted_index.magnetohydrodynamic | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.07510137 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |