Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
This work addresses probabilistic modeling for spectral data in planetary science, enabling applications like out-of-distribution detection, but it is incremental as it applies existing normalizing flow methods to a new domain.
The paper tackled the problem of constructing probability densities for high-dimensional spectral data, which is often intractable, by using normalizing flows on structured latent spaces, enabling realistic spectral sample generation and accurate state vector predictions with well-calibrated uncertainties on Mars rover ChemCam data.
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.