science_cases:lmsu_science_case
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science_cases:lmsu_science_case [2020/12/03 14:11] – david_parunakian | science_cases:lmsu_science_case [2022/09/12 13:44] (current) – admin | ||
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===== Aim of the science case ===== | ===== Aim of the science case ===== | ||
- | The MESSENGER spacecraft has completed over 4000 orbits around Mercury. The initial orbit had a 200 km periapsis altitude, 82.5 inclination, | + | The MESSENGER |
During each orbit, MESSENGER typically spent 1–2 h inside the magnetosphere; | During each orbit, MESSENGER typically spent 1–2 h inside the magnetosphere; | ||
Line 18: | Line 18: | ||
Magnetic field data are analyzed in Mercury solar orbital (MSO) coordinates. In MSO coordinates, | Magnetic field data are analyzed in Mercury solar orbital (MSO) coordinates. In MSO coordinates, | ||
- | The main task in this science | + | The goal of this case is to improve our understanding of Mercury' |
+ | |||
+ | ===== 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 | ||
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+ | The use of **statistical neural networks** allows us to explore another aspect: With the help of **[[: | ||
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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. | ||
+ | |||
+ | ^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 **[[: | ||
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+ | 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 | ||
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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 [[: | ||
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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 Magnetopause: | ||
+ | * 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: | ||
+ | * 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.1607001087.txt.gz · Last modified: 2020/12/03 14:11 by david_parunakian