1. Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
- Author
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Azari, Abigail R., Biersteker, John B., Dewey, Ryan M., Doran, Gary, Forsberg, Emily J., Harris, Camilla D. K., Kerner, Hannah R., Skinner, Katherine A., Smith, Andy W., Amini, Rashied, Cambioni, Saverio, Da Poian, Victoria, Garton, Tadhg M., Himes, Michael D., Millholland, Sarah, and Ruhunusiri, Suranga
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Earth and Planetary Astrophysics ,Statistics - Machine Learning - Abstract
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science., Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032
- Published
- 2020
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