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The SuperCam instrument for the Mars 2020 mission will include a Raman spectrometer, which will be the first time that measurements of this type have been performed on the Martian surface. Raman scattering produces peaks that correspond to the vibrational modes of the molecules in a sample, so it provides complementary information to LIBS and FTIR. The main difficulty in analysing this data is due to overlapping peaks and shoulders, which makes baseline correction extremely challenging. My talk will introduce the R package ‘serrsBayes,’ which uses informative Bayesian priors to fit a model of the peak broadening functions, while accounting for the baseline and additive white noise. We achieve this using a Rao-Blackwellized Particle Filter, which is a form of sequential Monte Carlo (SMC) algorithm. I will also discuss suitable sources of prior information, such as online databases of Raman spectra, a computational forward model known as time-dependent density functional theory (TD-DFT), as well as pre-flight calibration data. Host: Emily Casleton |