Exploring the Potential of Machine Learning in Space Exploration

Space-based capabilities have become one of the central themes in the technological discourse. Rapid proliferation of these capabilities has increased the significance of space exploration. As various space agencies and private entities expedite their ingress towards space, Machine Learning (ML) is becoming more relevant to ensure efficiency, safety and mission success. This paper examines the interplay between ML and space exploration, focusing on its key applications across three levels: near-Earth, solar system, and interstellar. The findings of this paper indicate that ML has major implications across all three levels of space exploration. In near-Earth applications, ML facilitates data collection and analysis, autonomous navigation, and development of Robonauts. At the solar system level, it plays a crucial role in planetary exploration, space weather forecasting, space debris identification, and asteroid trajectory prediction. Similarly, at the interstellar level, ML contributes to exoplanet detection, analysis of diffuse interstellar bands, and advancements in interstellar travel. However, while ML-driven applications offer substantial benefits, their implementation is hindered by various challenges arising from the inherent complexity of the space domain, necessitating targeted solutions for optimal utilisation.