Science Autonomy using Machine Learning for Astrobiology
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913770092101632 |
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| author | Da Poian, Victoria Theiling, Bethany Lyness, Eric Burtt, David Azari, Abigail R. Pasterski, Joey Chou, Luoth Trainer, Melissa Danell, Ryan Kaplan, Desmond Li, Xiang Clough, Lily McKinney, Brett Mandrake, Lukas Diamond, Bill Freissinet, Caroline |
| author_facet | Da Poian, Victoria Theiling, Bethany Lyness, Eric Burtt, David Azari, Abigail R. Pasterski, Joey Chou, Luoth Trainer, Melissa Danell, Ryan Kaplan, Desmond Li, Xiang Clough, Lily McKinney, Brett Mandrake, Lukas Diamond, Bill Freissinet, Caroline |
| contents | In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds. Advancing the integration of autonomy through AI and ML into space missions is a complex challenge, and we believe that by focusing on key areas, we can make significant progress and offer practical recommendations for tackling these obstacles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Science Autonomy using Machine Learning for Astrobiology Da Poian, Victoria Theiling, Bethany Lyness, Eric Burtt, David Azari, Abigail R. Pasterski, Joey Chou, Luoth Trainer, Melissa Danell, Ryan Kaplan, Desmond Li, Xiang Clough, Lily McKinney, Brett Mandrake, Lukas Diamond, Bill Freissinet, Caroline Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Artificial Intelligence Machine Learning In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially valuable in astrobiology applications, where models must distinguish biotic patterns from complex abiotic backgrounds. Advancing the integration of autonomy through AI and ML into space missions is a complex challenge, and we believe that by focusing on key areas, we can make significant progress and offer practical recommendations for tackling these obstacles. |
| title | Science Autonomy using Machine Learning for Astrobiology |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.00709 |