science_cases:lmsu_science_case
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===== Short description of the physical problem ===== | ===== Short description of the physical problem ===== | ||
- | <font 11ptfont-family: | + | The interplanetary medium is filled with the solar wind and the interplanetary magnetic field (IMF). |
- | <font 11ptfont-family: | + | The magnetopause |
- | <font 11ptfont-family: | + | IMF pressure + solar wind plasma pressure = planetary |
- | <font 11ptfont-family: | + | In the case of Mercury, |
+ | ===== Aim of the science case ===== | ||
- | <font 11ptfont-family: | + | The MESSENGER |
- | <font 11ptfont-family: | + | During each orbit, MESSENGER typically spent 1–2 h inside the magnetosphere; |
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+ | Magnetic field data are analyzed in Mercury solar orbital (MSO) coordinates. In MSO coordinates, | ||
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+ | The goal of this case is to improve our understanding of Mercury' | ||
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+ | ===== Description of the machine learning problem and our approach ===== | ||
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+ | Based on data from the mission, several global models of the magnetosphere were proposed (e.g., Winslow et al., 2013; Philpott et al., 2020). However, they could only describe an average shape of the bow shock and magnetopause crossings and can be prone to missing the statistical nuances in the data. Given large data, [[:glossary#neural_network|neural networks]] can be expected to approximate complex functions, which often surpass deterministic and rule-based methods, in a variety of time series tasks like [[:glossary# | ||
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+ | The use of **statistical neural networks** allows us to explore another aspect: With the help of **[[:glossary# | ||
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+ | The data set was **manually labelled with the boundary crossings**. To identify bow shocks, we first subtracted planetary dipole magnetic field components from the magnetometer measurements, | ||
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+ | The distribution of the different regions, after annotation, is reported in the following table. | ||
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+ | ^Label^Region^Statistical distribution (%)| | ||
+ | |0|Interplanetary magnetic field|65.4| | ||
+ | |1|Bow shock crossing|3.7| | ||
+ | |2|Magnetosheath|14.5| | ||
+ | |3|Magnetopause crossing|2.3| | ||
+ | |4|Magnetosphere|14.1| | ||
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+ | The boundaries of critical interest | ||
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+ | As a first step in pre-processing, [[:glossary# | ||
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+ | In the feature preparation stage, a sliding window of variable sizes (3 seconds to 3 minutes) with a hop size of 1 second was computed on the time series signal to obtain [[: | ||
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+ | The windowed features are fed first into a block of 3 [[:glossary# | ||
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+ | The window size used in these experiments | ||
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+ | Results of this science case were presented at the {{: | ||
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+ | **References: | ||
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+ | * Philpott, L.C., et al. (2020), The Shape of Mercury’s | ||
+ | * Winslow, R. M., et al. (2013), Mercurys magnetopause | ||
+ | * Fawaz, H.I., et al. (2019), Deep learning for time series classification: | ||
+ | * Lim, B., and Zohren, S. (2021), Time-series forecasting with deep learn- ing: a survey, Philosophical Transactions of the Royal Society A: Mathematical, | ||
+ | * Nguyen, V., et al. (2018), Applications of Anomaly Detection Using Deep Learning on Time Series Data. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/ | ||
science_cases/lmsu_science_case.1604483868.txt.gz · Last modified: 2020/11/04 10:57 by admin