Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra

TL;DR

This paper introduces a normalizing flow-based method for probabilistic modeling and uncertainty quantification of high-dimensional laser-induced breakdown spectroscopy data, demonstrated on Mars rover data to improve inference and calibration.

Contribution

It presents a novel application of normalizing flows to spectral data for density estimation and uncertainty quantification, enabling realistic sampling and accurate state predictions.

Findings

Generated realistic spectral samples

Accurately predicted state vectors with calibrated uncertainties

Demonstrated on Mars rover ChemCam data

Abstract

Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces to estimate such densities, enabling downstream inference tasks. In addition, we evaluate a method for uncertainty quantification when predicting unobserved state vectors associated with each spectrum. We demonstrate the capability of this approach on laser-induced breakdown spectroscopy data collected by the ChemCam instrument on the Mars rover Curiosity. Using our approach, we are able to generate realistic spectral samples and to accurately predict state vectors with associated well-calibrated uncertainties. We anticipate that this methodology will enable efficient probabilistic modeling of spectral data, leading to potential advances in several areas, including out-of-distribution detection and sensitivity analysis.