Estimating Scale Discrepancy in Bayesian Model Calibration for ChemCam on the Mars Curiosity Rover

TL;DR

This paper introduces a Bayesian calibration method that accounts for scale discrepancies in ChemCam LIBS data on Mars, improving the accuracy of physical parameter estimation.

Contribution

It presents a novel application of Bayesian model calibration incorporating a structured discrepancy model for ChemCam LIBS data analysis.

Findings

Effective discrepancy modeling improves calibration accuracy

Demonstrates systematic differences between simulated and observed spectra

Provides a general approach for theory-observation systematic differences

Abstract

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.