Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Abigail R. Azari; Andy W. Smith; Camilla D. K. Harris; Emily J. Forsberg; Gary Doran; Hannah R. Kerner; John B. Biersteker; Katherine A. Skinner; Michael D. Himes; Rashied Amini

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Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

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arxiv 2007.15129 v1 pith:ZSTMBBQC submitted 2020-07-29 astro-ph.IM astro-ph.EPstat.ML

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Abigail R. Azari , John B. Biersteker , Ryan M. Dewey , Gary Doran , Emily J. Forsberg , Camilla D. K. Harris , Hannah R. Kerner , Katherine A. Skinner

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Andy W. Smith Rashied Amini Saverio Cambioni Victoria Da Poian Tadhg M. Garton Michael D. Himes Sarah Millholland Suranga Ruhunusiri

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keywords planetarylearningmachinemethodsscienceabilityapplicationsbolstering

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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.

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

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