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Chemical and mineral compositions of asteroids reflect the formation andhistory of our Solar System. This knowledge is also important for planetarydefence and in-space resource utilisation. We aim to develop a fast and robustneural-network-based method for deriving the mineral modal and chemicalcompositions of silicate materials from their visible and near-infraredspectra. The method should be able to process raw spectra without significantpre-processing. We designed a convolutional neural network with two hiddenlayers for the analysis of the spectra, and trained it using labelledreflectance spectra. For the training, we used a dataset that consisted ofreflectance spectra of real silicate samples stored in the RELAB and C-Tapedatabases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, andolivine-pyroxene-rich meteorites. We used the model on two datasets. First, weevaluated the model reliability on a test dataset where we compared the modelclassification with known compositional reference values. The individualclassification results are mostly within 10 percentage-point intervals aroundthe correct values. Second, we classified the reflectance spectra of S-complex(Q-type and V-type, also including A-type) asteroids with known Bus-DeMeotaxonomy classes. The predicted mineral chemical composition of S-type andQ-type asteroids agree with the chemical composition of ordinary chondrites.The modal abundances of V-type and A-type asteroids show a dominantcontribution of orthopyroxene and olivine, respectively. Additionally, ourpredictions of the mineral modal composition of S-type and Q-type asteroidsshow an apparent depletion of olivine related to the attenuation of itsdiagnostic absorptions with space weathering. This trend is consistent withprevious results of the slower pyroxene response to space weathering relativeto olivine.

Neural network for determining an asteroid mineral composition from reflectance spectra