{"id":11604,"date":"2024-08-05T12:02:06","date_gmt":"2024-08-05T16:02:06","guid":{"rendered":"https:\/\/zobi.alcowep.com\/bourtagshdrevxnls658739\/nasa-trains-machine-learning-algorithm-for-mars-sample-analysis\/"},"modified":"2024-08-05T12:02:06","modified_gmt":"2024-08-05T16:02:06","slug":"nasa-trains-machine-learning-algorithm-for-mars-sample-analysis","status":"publish","type":"post","link":"https:\/\/zobi.alcowep.com\/bourtagshdrevxnls658739\/nasa-trains-machine-learning-algorithm-for-mars-sample-analysis\/","title":{"rendered":"NASA Trains Machine Learning Algorithm for Mars Sample Analysis"},"content":{"rendered":"
NASA Trains Machine Learning Algorithm for Mars Sample Analysis<\/h2>\n
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6 Min Read<\/div>\n
\n\t\t\t\t\t\t\t\tNASA Trains Machine Learning Algorithm for Mars Sample Analysis\t\t\t\t\t\t\t<\/h1>\n<\/p><\/div>\n<\/p><\/div>\n
The Mars Organic Molecule Analyzer, aboard the ExoMars mission’s Rosalind Franklin rover, will employ a machine learning algorithm to speed up specimen analysis.<\/figcaption><\/div>\n<\/p><\/div>\n
When the ESA (European Space Agency)-led Rosalind Franklin rover heads to Mars no earlier than 2028, a NASA machine learning algorithm gets its first chance to shine after more than a decade of data training in the lab.<\/li>\n
The Mars Organic Molecule Analyzer (MOMA), a mass spectrometer instrument aboard the rover, will analyze samples collected by a coring drill and send the results back to Earth, where they will be fed into the algorithm to identify organic compounds found in the samples.<\/li>\n
If any organic compounds are detected by the rover, the algorithm could greatly speed up the process of identifying them, saving scientists time as they decide the most efficient uses of the rover\u2019s time on the Red Planet.<\/li>\n<\/ul>\n
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.<\/p>\n
That\u2019s why researchers at NASA\u2019s 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\u2019s time on a planet.<\/p>\n
\u201cThis 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,\u201d said Xiang \u201cShawn\u201d Li, a mass spectrometry scientist in the Planetary Environments lab at NASA Goddard.<\/p>\n
The algorithm will first be put to the test with data from Mars, by operating on an Earth-bound computer using data collected by the Mars Organic Molecule Analyzer (MOMA) instrument.<\/p>\n
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