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The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756. Key Points: With a combination of feature extraction and repeated k‐means clustering applied to ChemCam LIBS data, we identify six clusters representing dominant compositions in Gale craterThe cluster detections are in agreement with the chemostratigraphy explored by CuriosityWe successfully trained a random forest classifier with the obtained cluster labels and used it to classify recent ChemCam targets [ABSTRACT FROM AUTHOR]

- The 'Cosmic Dust Catalog,' published by the NASA Johnson Space Center (JSC), describes thousands of interplanetary dust particles subjected to preliminary analysis and with labels indicating their origin. However, only about 80% of the particles are assigned unambiguous labels, the labels of the remaining 20% being uncertain. In addition, the Stardust mission results opened up the possibility that some particles classified as terrestrial contaminants are instead of cosmic (cometary) origin. In this article, we present a methodology for automatic classification of particles on the basis of similarity of their X-ray energy dispersive spectrometry spectra. The method is applied to the 467 particles constituting Volume 15 of the catalog. A first part of the analysis is to digitize the spectra from their scanned images. The digitized spectra are subjected to agglomerative clustering, which reveals 16 distinct clusters or compositional types of particles. The Sammon's map is used to visualize the relationship between different clusters; 6 clusters corresponding to cosmic particles and 10 clusters corresponding to terrestrial contaminants are clearly separated on the map indicating overall differences between diverse spectra of cosmic and terrestrial particles. By reconciling labels with the clustering structures, we propose the relabeling of 155 particles including the relabeling of 31 terrestrial contaminants into cosmic particles. The proposed relabeling needs to be confirmed by in-depth study of these particles. The paucity of particles with firmly determined cometary or asteroidal origin makes it difficult to establish whether the spectra based autoclassification can be utilized to discriminate between cometary and asteroidal particles. The methodology presented here can be used to classify all particles published in the catalog, as well as different samples for which comparable spectra are available. [ABSTRACT FROM AUTHOR]