Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics
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by
A. R. Azari, J. W. Lockhart, M. W. Liemohn, X. Jia
2020
Abstract
Recent improvements in data collection volume from planetary and space
physics missions have allowed the application of novel data science techniques.
The Cassini mission for example collected over 600 gigabytes of scientific data
from 2004 to 2017. This represents a surge of data on the Saturn system.
Machine learning can help scientists work with data on this larger scale.
Unlike many applications of machine learning, a primary use in planetary space
physics applications is to infer behavior about the system itself. This raises
three concerns: first, the performance of the machine learning model, second,
the need for interpretable applications to answer scientific questions, and
third, how characteristics of spacecraft data change these applications. In
comparison to these concerns, uses of black box or un-interpretable machine
learning methods tend toward evaluations of performance only either ignoring
the underlying physical process or, less often, providing misleading
explanations for it. We build off a previous effort applying a semi-supervised
physics-based classification of plasma instabilities in Saturn's magnetosphere.
We then use this previous effort in comparison to other machine learning
classifiers with varying data size access, and physical information access. We
show that incorporating knowledge of these orbiting spacecraft data
characteristics improves the performance and interpretability of machine
learning methods, which is essential for deriving scientific meaning. Building
on these findings, we present a framework on incorporating physics knowledge
into machine learning problems targeting semi-supervised classification for
space physics data in planetary environments. These findings present a path
forward for incorporating physical knowledge into space physics and planetary
mission data analyses for scientific discovery.
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