激光诱导击穿光谱的神经网络密度估计和不确定度量化
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
摘要：
在高维谱数据中构造用于推断的概率密度通常是棘手的。在这项工作中，我们使用结构化谱潜在空间上的归一化流来估计这种密度，从而实现下游推理任务。此外，我们评估的方法不确定性量化时，预测未观测到的状态向量与每个光谱。我们证明了这种方法的激光诱导击穿光谱数据收集的ChemCam仪器上的火星漫游车Currency的能力。使用我们的方法，我们能够生成现实的光谱样本，并准确地预测状态向量与相关的校准良好的不确定性。我们预计，这种方法将使有效的光谱数据的概率建模，导致潜在的进步，在几个领域，包括分布外检测和灵敏度分析。
英文摘要：
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.
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