AI in Astronomy
Last updated
Last updated
Astronomy deals with massive datasets collected from telescopes, space probes, and other observational instruments. AI is a powerful tool for handling this data, making it easier to discover and understand cosmic phenomena.
NASA’s Kepler Space Telescope gathered data on thousands of stars in search of exoplanets (planets outside our solar system). However, identifying exoplanets involves sifting through vast amounts of data to detect tiny dips in starlight, a sign that a planet is passing in front of its star. In 2017, Google researchers applied deep learning models, specifically TensorFlow, to analyze this data. Their AI model discovered two new exoplanets that had previously been missed by traditional analysis techniques. This demonstrated the ability of AI to enhance our understanding of distant solar systems.
AI systems are also used in identifying supernovae—massive stellar explosions that occur infrequently. The Zwicky Transient Facility (ZTF), an astronomical survey at Caltech, uses machine learning algorithms to scan sky survey data for potential supernovae. By automating this process, astronomers are able to detect these transient events in real time, enabling rapid follow-up observations.
Gravitational waves, first detected in 2015, are ripples in spacetime caused by massive cosmic events such as black hole collisions. AI has been essential in filtering noise from gravitational wave detectors. The Laser Interferometer Gravitational-Wave Observatory (LIGO) uses AI-based signal processing to increase the sensitivity of its instruments, allowing for more precise detection of these rare phenomena.
