NASA has sent many robotic spacecraft to perform remote-sensing observations of surface composition from orbit. This includes gamma-ray observations of planetary surfaces, which provide position-dependent energy spectra whose shape is due to the sum of components from different elements. We seek to develop a machine learning (ML) approach to isolate the element-dependent contributions to the measurements as a function of surface location. We will use the Lunar Prospector Gamma-Ray Spectrometer dataset to train a model using the moon as the training dataset. The first objective is to identify the best ML model approach and quantify its accuracy as this data, and the Moon, are well understood. We will then use domain adaptation to extend the approach to other planetary objects that are less well known, enabling new discoveries.
Total project length: 175/350 hours.
Please use this link to access the test for this project. The test is due by April 3rd, however please keep in mind that it takes about 1 week to craft a good proposal and proposals need to be submitted via GSoC portal by April 4
Python and relevant past experience in Machine Learning.
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