论文标题
具有变异自动编码器神经网络的光发射光谱中的脱氧和特征提取
Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks
论文作者
论文摘要
近年来，独特的机器学习（ML）模型已被分别用于从原始角度分辨光发射光谱（ARPES）数据获得的能量摩托米分散强度图中的特征提取和降噪。在这项工作中，我们采用了浅层变异自动编码器（VAE）神经网络来证明使用ML进行降级和从ARPES分散图中提取特征的前景。
In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy-momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In this work, we employ a shallow variational auto-encoder (VAE) neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.
