Bethany P. Theiling; Luoth Chou; Victoria Da Poian; Melissa Battler; Kaizad Raimalwala; Ricardo Arevalo; Marc Neveu; Ziqin Ni; Heather Graham; Jamie Elsila; Barbara Thompson · 2022 · Astrobiology
Paper
Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” ( e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.
Analysis
This paper explores the potential of machine learning and science autonomy to enhance astrobiology missions to ocean worlds by enabling onboard data analysis, instrument optimization, and real-time decision-making.
Discovery
Nancy Y. Kiang; Christopher Gisriel; Robert E. Blankenship; Mary N. Parenteau; Hazel A. Barton; Oded Béjà; Anthony J. Burnetti; Ligia F. Coelho; Saleheh Ebadirad; Nathan M. Ennist; Yuka Fujii; Colin Gates; R. J. Graham; Keiichi Inoue; Betül Kaçar; Yu Komatsu; Émilie A. Laflèche; Nicoletta La Rocca; Elisabetta Liistro; Jonathan Lindsey; Timothy Lyons; Carolina A. Martinez-Gutierrez; Taro Matsuo; Victoria Meadows; Gary F. Moore; Massimo Olivucci; Kevin E. Redding; Edward W. Schwieterman; Tejinder Singh; Kenji Takizawa; Anna Grace Ulses; Junko Yano; Felisa Wolfe-Simon; Michael L. Wong; Anastasia G. Yanchilina
Peter M. Higgins; Weibin Chen; Oliver Warr; Lucas M. Fifer; Wanying Kang; Charles S. Cockell; Barbara Sherwood Lollar
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Conor A. Nixon; Samuel Birch; Audrey Chatain; Charles Cockell; Kendra K. Farnsworth; Peter M. Higgins; Stéphane Le Mouélic; Rosaly M. C. Lopes; Michael J. Malaska; Mohit Melwani Daswani; Kelly E. Miller; Catherine D. Neish; Olaf G. Podlaha; Jani Radebaugh; Lauren R. Schurmeier; Ashley Schoenfeld; Krista M. Soderlund; Anezina Solomonidou; Christophe Sotin; Nicholas A. Teanby; Tetsuya Tokano; Steven D. Vance
Niki Parenteau; Giada Arney; Eleanora Alei; Ruslan Belikov; Svetlana; Berdyugina; Dawn Cardace; Ligia F. Coelho; Kevin Fogarty; Kenneth Gordon; Jonathan Grone; Natalie Hinkel; Nancy Kiang; Ravi Kopparapu; Joshua Krissansen-Totton; Emilie LaFleche; Jacob Lustig-Yaeger; Eric Mamajek; Avi Mandell; Taro Matsuo; Connor Metz; Mark Moussa; Stephanie Olson; Lucas Patty; Bill Philpot; Sukrit Ranjan; Edward Schwieterman; Clara Sousa-Silva; Anna Grace Ulses; Sara Walker; Daniel Whitt
Saggio D; Phung CL; Bravo M; Corcoran TC; Snyder JC; Stieber SCE
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