Machine Learning Applications in the Search for Life Beyond Earth

Machine learning (ML) and artificial intelligence (AI) have moved beyond niche applications to become transformative and essential tools for data analysis and pattern recognition in many scientific fields. Astrobiology stands to benefit significantly from these advances in that ML and AI offers powerful solutions for analyzing complex datasets, understanding intricate systems, and developing autonomous spacecraft operations, accelerating the quest for extraterrestrial life.This thematic collection of Frontiers in Astronomy and Space Sciences focuses on the applications of ML and AI in astrobiology, highlighting key research directions that can transform the field. We seek contributions that explore:• The use of ML and AI for analyzing both sparse yet information-rich datasets and large-scale astronomical data;• Techniques for discovering the unknown, such as identifying “Life as We Don’t Know It (Yet)” or technosignatures through anomaly detection;• New ML or AI advancements for equipping spacecraft with autonomous systems that minimize human intervention;• Verification and validation techniques relevant to life detection;• The robustness, interpretability, and limitations of ML models in astrobiological research; and• Science communication and outreach efforts related to ML and AI development for astrobiology.This Research Topic aims to support astrobiologists in harnessing data from ground based and orbiting observatories such as the James Webb Space Telescope, Keck Observatory, Vera C. Rubin Observatory, and the future NASA Habitable Worlds Observatory and ESA Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL), as well as planetary science missions such as NASA’s Europa Clipper and ESA’s Jupiter Icy Moons Explorer (JUICE). The research published here will equip the next generation of astrobiologists with the knowledge and tools necessary to potentially make one of humanity’s most profound discoveries—the detection of life beyond Earth. This volume encourages submissions demonstrating interdisciplinary collaboration between astrobiologists, computer and data scientists, and astronomers, fostering a research community ready to meet the challenges and opportunities at the intersection of astrobiology and ML/AI. Topic editors: Hermine Landt, Miriam Rengel, Bethany Theiling, Kai Hou Yip, Caleb Scharf Source: https://www.frontiersin.org/research-topics/67403/machine-learning-applications-in-the-search-for-life-beyond-earth