Planetary scientists are finding creative ways to use machine learning.
Machine learning is a way of training computers to identify patterns in data, then harness those patterns to make decisions, predictions or classifications. Another major advantage to computers — besides not requiring life-support — is their speed. For many tasks in astronomy, it can take humans months, years or even decades of effort to sift through all the necessary data. One example is identifying boulders in pictures of other planets.
"You can find up to 10,000, hundreds of thousands of boulders, and it's very time consuming," Nils Prieur, a planetary scientist at Stanford University in California said during his talk at AGU. Prieur's new machine-learning algorithm can detect boulders across the wholein only 30 minutes. It's important to know where these large chunks of rock are to make sure new missions can land safely at their destinations.
Computers can identify a number of other planetary phenomena, too: explosive volcanoes on Mercury, vortexes inDuring the conference, planetary scientist Ethan Duncan, from NASA's Goddard Space Flight Center in Maryland, demonstrated how machine learning can identify not chunks of rock, but chunks ofon Jupiter's icy moon Europa. The so-called chaos terrain is a messy-looking swath of Europa's surface, with bright ice chunks strewn about a darker background.
Upcoming missions could also incorporate artificial intelligence as part of the team, using this tech to empower probes to make real-time responses to hazards and even land autonomously. Landing is a notorious challenge for spacecraft, and always one of the most dangerous times of a mission. “The 'seven minutes of terror' on Mars [during descent and landing], that's something we talk about a lot,” Bethany Theiling, a planetary scientist at NASA Goddard, said during her talk."That gets much more complicated as you get further into the solar system. We have many hours of delay in communication."
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