Title: A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data Show Chinese title

Title: 一种多尺度贝叶斯方法用于能量色散X射线数据的量化和去噪

Abstract: Energy dispersive X-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected X-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO3 sample, such that ferroelectric domains are mapped with unit-cell resolution. Show Chinese abstract

Abstract: 能量色散X射线（EDX）谱成像能够以原子级的空间分辨率提供成分信息。 然而，实验限制通常会产生极其稀疏和噪声很大的EDX谱。 在这些条件下，必须利用每一个检测到的X射线以获得关于样品的最大可能信息量。 为此，我们引入了一种稳健的多尺度贝叶斯方法，该方法考虑了EDX数据中的泊松统计特性，并利用了其潜在的空间相关性。 这与EDX光谱模拟（元素贡献和韧致辐射背景）结合成一种贝叶斯估计策略。 当使用模拟数据集进行测试时，通过这种方法获得的化学图更加准确，并且保留了更高的空间分辨率，优于标准方法获得的结果。 这些特性也适用于实验数据集，其中该方法增强了典型四角形铁电PbTiO3样品的原子分辨率化学图，使得铁电畴能够以单胞分辨率进行映射。