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Constructing probability densities for inference in high-dimensional spectraldata is often intractable. In this work, we use normalizing flows on structuredspectral latent spaces to estimate such densities, enabling downstreaminference tasks. In addition, we evaluate a method for uncertaintyquantification when predicting unobserved state vectors associated with eachspectrum. We demonstrate the capability of this approach on laser-inducedbreakdown spectroscopy data collected by the ChemCam instrument on the Marsrover Curiosity. Using our approach, we are able to generate realistic spectralsamples and to accurately predict state vectors with associated well-calibrateduncertainties. We anticipate that this methodology will enable efficientprobabilistic modeling of spectral data, leading to potential advances inseveral areas, including out-of-distribution detection and sensitivityanalysis.

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