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A reliable classification of meteorites is of great importance in order to study the meteorite history. Secondary ion mass spectrometry is applied to collect a database of spectra for four meteorite classes, H chondrite, IAB iron, L chondrite, and achondrite and based on convolutional neural network to determine the meteorite class of un unknown sample. The focus of the presented analysis is on the Kosice meteorite which is identified with following elements Li, B, Na, Mg, Al, Si, P, K, Ca, Ti, Cr, Mn, Fe, and Ni. Due to the high mass resolution of 7000, several isotopes of these elements are clearly identified. The Secondary ion mass spectrometry images provide also mineralogical analysis by revealing distributions of chemical composition. The minerals of albite, troilite, augite, and diopside are recognized. Using 88 reference spectra, the unknown sample is identified as the H chondrite based on calculated probability. Secondary ion mass spectrometry technique coupled with convolutional neural network proves to be the advanced tool to analyze meteorites, to classify them, and to determine a meteorite of unknown origin. |
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