|
Abstract:
|
We have developed a novel statistical method to address a fundamental scientific goal: disaggregation, or estimation of the composition of an unknown aggregate target. By combining forward (computer) models of the target of interest with measured data, our approach enables computer-model calibration techniques to directly solve the disaggregation problem. We develop our method in the context of chemical spectra generated by laser-induced breakdown spectroscopy (LIBS), used by instruments such as ChemCam on the Mars Rover Curiosity. Because a single run of the LIBS computer model may take hours on parallel computing platforms, we build fast emulators for single-compound targets. We then construct multi-compound emulators by combining the single-compound emulators in a hierarchical model. Our approach yields the first statistical characterization of matrix effects, i.e. spectral peaks that are amplified or suppressed when compounds are combined in a target versus measured in isolation, and the first capability in uncertainty quantification (UQ) that addresses the unique challenges of chemical spectra.
|
