Self-Organized Maps in Scientific Data Analysis

Details Self-Organized Maps in Scientific Data Analysis Self-Organized Maps in Scientific Data Analysis

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsm11b1612s&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SM11B-1612

Physics

Geophysics

3200 Mathematical Geophysics (0500, 4400, 7833)

Scientific paper

The Thermal Ion Dynamics Experiment (TIDE) investigates low energy (0.1 - 450 eV) plasma in the Earth's magnetosphere, especially in the polar regions. It is part of NASA's larger Polar mission. After six months in orbit it became necessary for TIDE to operate in a mode that did not directly provide mass discrimination. However, in this mode, energy-time and spin-time spectrograms of differential ion flux were routinely available. The number of peaks in the energy-time spectrograms relates to the composition of the plasma. Kohonen self-organized maps (SOMs,) a type of neural network, are particularly suited to this problem due to the amount of data that needs to be analyzed and the algorithm's ability to find patterns within data. The algorithm leads to clustering of similar data points on the map. Ultimately, the location of the input data point on the map allows for determination of how many peaks the data point contains, and thus the composition of the plasma at that time. The SOM correctly classified 99% of the input data, making it a viable solution to the problem. Further research is planned, namely the possibility of extending this concept to investigate energetic neural atom (ENA) images in order to determine the source of these atoms.

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