Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics.

Area of Science:

• Planetary science and space physics.
• Application of data science techniques to analyze large-scale mission data.

Background:

• Planetary missions like Cassini generate massive datasets (e.g., 600 GB), necessitating advanced data analysis methods.
• Traditional machine learning often focuses on performance, potentially overlooking interpretability and physical context crucial for scientific inference.

Purpose of the Study:

• To investigate the impact of incorporating spacecraft data characteristics into machine learning models for space physics applications.
• To enhance the performance and interpretability of machine learning methods for analyzing planetary mission data.

Main Methods:

• Utilized Cassini mission data as a case study for analyzing Saturn's magnetosphere.
• Compared a semi-supervised physics-based classification approach with other machine learning classifiers under varying data and physics information access.
• Developed a framework for integrating physics knowledge into machine learning for semi-supervised classification.

Main Results:

• Incorporating knowledge of orbiting spacecraft data characteristics significantly improves machine learning performance and interpretability.
• Physics-informed machine learning methods are essential for deriving meaningful scientific insights from complex space physics data.

Conclusions:

• A framework for integrating physics knowledge into machine learning is presented for space physics data analysis.
• This approach offers a path forward for scientific discovery in planetary missions by enhancing data analysis capabilities.