AI in Space: NASA Studying Exoplanets, ESA Supporting Satellites

By AI Trends Staff
AI is being employed in a wide range of efforts to explore and study space, including the study of exoplanets by NASA, the support of satellites by ESA, development of an empathic assistant for astronauts and efforts to track space debris.
NASA scientists are partnering with AI experts from companies including Intel, IBM and Google to apply advanced computer algorithms to problems in space science.
Machine learning is seen as helping space scientists to learn from data generated by telescopes and observatories such as the James Webb Space Telescope, according to a recent account from NASA. “These technologies are very important, especially for big data sets and in the exoplanet field,” stated Giada Arney, an astrobiologist at NASA’s Goddard Space Flight Center in Greenbelt, Md. (Exoplanets are beyond the solar system.)  “Because the data we’re going to get from future observations is going to be sparse and noisy, really hard to understand So using these kinds of tools has so much potential to help us.”
Giada Arney, astrobiologist, NASA’s Goddard Space Flight Center NASA has laid some groundwork for collaborating with private industry. For the past four summers, NASA’s Frontier Development Lab (FDL) has brought together technology and space innovators for eight weeks every summer to brainstorm and develop code. The program is a partnership between the SETI Institute and NASA’s Ames Research Center, both located in Silicon Valley.
The program pairs science and computer engineering early-career doctoral students with experts from the space agency, academia and some big tech companies. The companies contribute hardware,  algorithms, supercomputing resources funding, facilities and subject matter experts. Some of the resulting technology has been put to use, helping to identify asteroids, find planets and predict extreme solar radiation events.
Scientists at Goddard have been using different techniques to reveal the chemistry of exoplanets, based on the wavelengths of light emitted or absorbed by molecules in their atmospheres. With thousands of exoplanets discovered so far, the ability to make quick decisions about which ones deserve further study would be a plus.
Arney, working with Shawn Domagal-Goldman, an astrobiologist at Goddard Center, working with technical support from Google Cloud, deployed a neural network to compare performance to a machine learning approach. University of Oxford computer science graduate student Adam Cobb led a study to test the capability of a neural network against a widely-used machine learning technique known as a “random forest.” The team analyzed the atmosphere of WASP-12b, an exoplanet discovered in 2008 that had a comparison study done with a random forest technique, using data supplied by NASA’s Hubble Space Telescope.
“We found out right away that the neural network had better accuracy than random forest in...