Machine Learning for Planetary Science

"Machine Learning for Planetary Science" by Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner is a astronomy book and space science reference focused on Deep Sky & Solar System. Best for students, researchers, and serious astronomy enthusiasts.

Topic: Deep Sky & Solar System

Author: Joern Helbert, Mario D'Amore, Michael Aye, Hannah Kerner

Who this is for:

• Astronomy students
• Researchers and advanced hobbyists
• Readers exploring space science topics

Why this book matters: It matters because it helps readers build a stronger understanding of astronomy concepts, observations, and scientific ideas related to space.

"Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation."--