GREENBELT, Md. - When a robotic rover lands on another world, scientists have a limited amount of time to collect data from the troves of explorable material, because of short mission durations and the length of time to complete complex experiments.
That’s why researchers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, are investigating the use of machine learning to assist in the rapid analysis of data from rover samples and help scientists back on Earth strategize the most efficient use of a rover’s time on a planet, Matthew Kaufman writes for NASA. Continue reading original article.
The Military & Aerospace Electronics take:
6 August 2024 - “This machine learning algorithm can help us by quickly filtering the data and pointing out which data are likely to be the most interesting or important for us to examine,” said Xiang “Shawn” Li, a mass spectrometry scientist in the Planetary Environments lab at NASA Goddard.
“For example, if we measure a sample that shows signs of large, complex organic compounds mixed into particular minerals, we may want to do more analysis on that sample, or even recommend that the rover collect another sample with its coring drill,” Li said.
NASA and the broader scientific community are heavily invested in the search for evidence of life, both past and present, on celestial bodies beyond Earth. Li and Da Poian believe their algorithm could be instrumental in future missions to promising locations like Saturn's moons Titan and Enceladus, as well as Jupiter's moon Europa.
Their ultimate objective is to develop highly advanced "science autonomy." This would enable mass spectrometers to not only analyze their data independently but also make operational decisions on their own, significantly boosting the efficiency of scientific research and mission operations.
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Jamie Whitney, Senior Editor
Military + Aerospace Electronics
