STRUCTURED DISCREPANCY IN BAYESIAN MODEL CALIBRATION FOR CHEMCAM ON THE MARS CURIOSITY ROVER

• 成果类型: Article
• 署名作者: Bhat, K. Sham; Myers, Kary; Lawrence, Earl; Colgan, James; Judge, Elizabeth
• 署名单位: United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1373
• 发表日期: 2020
• 页码: 2020-2036
• 关键词: instrument suite; emission-spectra; science; unit

摘要

The Mars rover Curiosity carries an instrument called ChemCam to determine the composition of the soil and rocks via laser-induced breakdown spectroscopy (LIBS). Los Alamos National Laboratory has developed a simulation capability that can predict spectra from ChemCam, but there are majorscale 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 and an illustration of the importance of setting scientifically informed and constrained discrepancy models within Bayesian model calibration.