science_cases:lmsu_science_case
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===== Description of the machine learning problem and our approach ===== | ===== Description of the machine learning problem and our approach ===== | ||
- | 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, 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 classification (Fawaz et al., 2019), time series forecasting (Lim and Bohren, 2021), and rare time series event detection (Nguyen et al., 2018). We leverage these to **develop a predictor** that can be used in real-time during orbit to predict magnetic region for each step in a short window of observation. | + | 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, [[: |
- | The use of **statistical neural networks** allows us to explore another aspect: With the help of **active learning**, it is possible to add samples to the training process incrementally. With this, we can examine how the model scales its predictive capacity with increasing data, and thus study how the variations such as changing solar wind and environmental conditions affects the manifestation of boundary signatures. To begin with, different orbits can be expected to have some element of similarity in the magnetic field structure, yet would have large variations in the same segments at different conditions. It is also interesting to study what the minimum amount is for the data needed to be able to generalise these phenomena for future missions such as BepiColombo. | + | 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 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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|4|Magnetosphere|14.1| | |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 **data imbalance issue** that requires investigating special techniques to ensure the predictor does not bias towards the overrepresented classes. | + | 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, | + | As a first step in pre-processing, |
- | 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 feature vectors. Finally, the features were normalised to have mean of 0 and a standard deviation of 1. No other pre-processing or engineering was applied in order to allow the deep learning model to engineer features implicitly. | + | 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 |
- | The windowed features are fed first into a block of 3 Convolutional | + | The windowed features are fed first into a block of 3 [[: |
- | The window size used in these experiments is 30 seconds. Overall, the **predictor | + | The window size used in these experiments is 30 seconds. Overall, the [[: |
- | The results from the active learning experiment are still not complete. We are currently in the process of documenting them and we will put them forth in a publication soon. | + | Results of this science case were presented at the {{: |
- | + | ||
- | Results of this science case were presented at the EGU21 as well as at EPSC2021 | + | |
**References: | **References: |
science_cases/lmsu_science_case.1645091422.txt.gz · Last modified: 2022/02/17 10:50 by admin