The search for extraterrestrial intelligence (SETI) is a field that has long been within the domain of traditional signal processing techniques.
AI
Machine-learning Identified Molecular Fragments Responsible For Infrared Emission Features Of Polycyclic Aromatic Hydrocarbons
Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral polycyclic aromatic hydrocarbons (PAHs).
Training Robots How To Learn And Make Their Own Decisions On The Fly
Mars rovers have teams of human experts on Earth telling them what to do. But robots on lander missions to moons orbiting Saturn or Jupiter are too far away to […]
Combining Multi-spectral Data With Statistical And Deep-learning Models For Improved Exoplanet Detection In Direct Imaging At High Contrast
Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star. […]
Discovery of 69 New Exoplanets Using Machine Learning
In a groundbreaking achievement, a team of machine learning scientists and astronomers from Universities Space Research Association (USRA), the SETI Institute, and NASA discovered 69 new exoplanets using advanced machine […]
AI Could Deceive Us As Much As The Human Eye Does In The Search For Extraterrestrials
An artificial neural network has identified a square structure within a triangular one in a crater on the dwarf planet Ceres, with several people agreeing on this perception. The result […]
