A new "deep learning" algorithm
The software, called PlanetNet, mapped out a monster 2008 Saturn storm system in detail using data gathered by NASA's Cassini spacecraft which studied the ringed planet up close to 2004 through 2017.
"Missions like Cassini gather enormous amounts of data, but classical techniques for analysis have drawbacks, either in the accuracy Ingo Waldmann, deputy director of the Center for Space and Exoplanet Data at University College London in England, said in a statement.
Related: Amazing Saturn Photos From NASA's Cassini Orbiter
"This gives us the potential to analyze the phenomena of large areas […]
PlanetNet searches for evidence of "clustering" in cloud structure and atmospheric composition, then uses such information to generate precise maps. Waldmann and study co-leader Caitlin Griffith, of the University of Arizona's Lunar and Planetary Laboratory, trained and tested the algorithm using data gathered by Cassini's Visible and Infrared Mapping Spectrometer (VIMS) instrument.
For the new study, which was published online today (April 29) in the journal Nature Astronomy the duo chose a data set containing VIMS observations of a multiple-storm system that boiled on Saturn in February 2008. the system was complex and quite large.
PlanetNet took this information and ran it, providing new insights into the storms. S-shaped cloud of ammonia. What a great deal. Saturn's atmosphere, the researchers said.
"Saturn ", "Griffith said in the same statement"
"PlanetNet enables us to analyze much larger volumes of data, and this gives insights into the large-scale dynamics." PlanetNet can easily be adapted to other datasets and planets, making it an invaluable potential tool for many future missions. "
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