Pixelate Novel Visualization And Computational Methods For The Analysis Of Astrobiological Spectroscopy Data

AGU Astrobiology Science Conference

Among the Mars 2020 Rover’s primary science objectives is the use of micro-X-ray fluorescence (microXRF) spectroscopy to search for biological mineral indicators. When interpreting microXRF data, astrobiologists must extrapolate mineral composition from elemental abundance. Current methods of analysis often rely on time-intensive practices such as manual spectrum peak labeling, command-line visualization tools, and producing standalone heatmaps of a sample area to determine spatial mineral distributions. Further, as data volume increases with sensor resolution, inefficient visualization techniques can easily obscure minute yet geochemically significant features within a sample. In conjunction with the Planetary Instrument for X-ray Lithochemistry (PIXL) science team, we have developed a data management, analysis, and visualization tool that improves the speed, fluidity, and effectiveness of analyzing microXRF data for astrobiology. Our tool, called PIXL Element Analysis Technology (PIXELATE), leverages novel advances in front-end rendering, data manipulation, and interactivity––as well as statistical and machine learning methods––in an effort to explore the application of modern data science methods to the PIXL research process.