2020/04/08 by K. Sham Bhat, Kary Myers, Bhat, K. Sham +9
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Applications (stat.AP) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Scientific Measurement and Uncertainty Evaluation #Statistical Mechanics and Entropy #stat.AP
paper · pdf · doi:10.48550/arxiv.2004.04301
21 pages, 10 Figures, submitted to the Annals of Applied Statistics
arxiv created 2020/04/08 · openalex publication_date 2020/04/08 · arxiv updated 2020/04/10 · openalex created_date 2020/04/17 · openalex updated_date 2026/07/28
The Mars rover Curiosity carries an instrument called ChemCam to determine the composition of the soil and rocks. ChemCam uses laser-induced breakdown spectroscopy (LIBS) for this purpose. Los Alamos National Laboratory has developed a simulation capability that can predict spectra from ChemCam, but there are major scale differences between the prediction and observation. This presents a challenge when using Bayesian model calibration to determine the unknown physical parameters that describe the LIBS observations. We present an analysis of LIBS data to support ChemCam based on including a structured discrepancy model in a Bayesian model calibration scheme. This is both a novel application of Bayesian model calibration and a general purpose approach to accounting for such systematic differences between theory and observation in this setting.