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.

翻译：构建高维光谱数据的推断概率密度往往是难以解决的。 在这项工作中,我们利用结构化光谱潜伏空间的正常流动来估计这种密度,从而能够进行下游推论任务。 此外,我们评估了在预测与每个频谱相关的未观测状态矢量时的不确定性量化方法。我们展示了这一方法对由ChemCam仪器在火星光谱曲线上收集的激光诱发分解光谱分析数据的能力。我们利用这一方法,能够生成现实的光谱样本,并准确预测与相关校准的不确定性相关的向量。我们预计,这一方法将能够对光谱数据进行有效的概率建模,导致在若干领域取得潜在进展,包括分流外探测和敏感度分析。