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

Autor:	Kontolati, Katiana, Klein, Natalie, Panda, Nishant, Oyen, Diane
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Mathematics - Spectral Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.08709 Zobrazit plný text záznamu View this record from Arxiv