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 and magnetospheric |
- | <font 11ptfont-family: | + | In the case of Mercury, in front of the magnetopause the solar wind ram pressure, and inside it the magnetospheric magnetic field pressure play the main roles. Mercury' |
+ | ===== Aim of the science case ===== | ||
- | <font 11ptfont-family: | + | The MESSENGER (MErcury Surface, Space ENvironment, |
- | <font 11ptfont-family: | + | During each orbit, MESSENGER typically spent 1–2 h inside the magnetosphere; the rest of the time was spent in the magnetosheath |
- | <font 11ptfont-family: | + | Magnetic field data are analyzed in Mercury |
- | <font 11ptfont-family: | + | The goal of this case is to improve our understanding of Mercury' |
- | <font 11ptfont-family: | + | ===== Description of the machine learning problem |
- | ===== Aim of the science case ===== | + | Based on data from the mission, several global models |
+ | |||
+ | The use of **statistical neural networks** allows us to explore another aspect: With the help of **[[: | ||
+ | |||
+ | 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, | ||
+ | |||
+ | The distribution of the different regions, after annotation, is reported in the following table. | ||
+ | |||
+ | ^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| | ||
+ | |||
+ | The boundaries of critical interest - bow shock and magnetopause - are minorities with only 3.7 and 2.3 % representation. The table highlights the **[[: | ||
+ | |||
+ | As a first step in pre-processing, | ||
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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 [[: | ||
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+ | The window size used in these experiments is 30 seconds. Overall, the [[: | ||
+ | |||
+ | Results of this science case were presented at the {{: | ||
+ | |||
+ | **References: | ||
- | <font 11ptfont-family:Arial;color:# | + | * Philpott, L.C., et al. (2020), The Shape of Mercury’s Magnetopause: The Picture From MESSENGER Magnetometer Observations and Future Prospects for BepiColombo, |
+ | * Winslow, R. M., et al. (2013), Mercurys magnetopause and bow shock from MESSENGER Magnetometer observations, | ||
+ | * Fawaz, H.I., et al. (2019), Deep learning for time series classification: a review, Data Mining and Knowledge Discovery 33, doi: 10.1007/s10618-019-00619-1 | ||
+ | * 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 | ||
science_cases/lmsu_science_case.1604487462.txt.gz · Last modified: 2020/11/04 11:57 by admin