论文标题
无监督的现实世界知识提取通过散开的变异自动编码器用于光子诊断
Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnostics
论文作者
论文摘要
我们通过神经网络对测量的电子飞行时间数据介绍了现实世界数据处理。具体而言，在汉堡的自由电子激光闪光灯上使用诊断仪器的数据中使用分离的变分自动编码器。没有A-Priori知识，网络能够找到具有较低信噪比的单发FEL光谱的表示形式。这揭示了有关光子特性的关键信息，以直接的人解剖方式揭示了这一点。确定了中央光子能量以及强度以及非常特异性的特征。该网络还能够清洁数据，即denoising以及删除人工制品。在重建中，这允许识别强度非常低的签名，这在原始数据中几乎无法识别。在这种特殊情况下，网络在Flash处提高了诊断分析的质量。但是，这种无监督的方法还具有改善对其他类似类型的光谱数据的分析。
We present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument for online wavelength monitoring at the free electron laser FLASH in Hamburg. Without a-priori knowledge the network is able to find representations of single-shot FEL spectra, which have a low signal-to-noise ratio. This reveals, in a directly human-interpretable way, crucial information about the photon properties. The central photon energy and the intensity as well as very detector-specific features are identified. The network is also capable of data cleaning, i.e. denoising, as well as the removal of artefacts. In the reconstruction, this allows for identification of signatures with very low intensity which are hardly recognisable in the raw data. In this particular case, the network enhances the quality of the diagnostic analysis at FLASH. However, this unsupervised method also has the potential to improve the analysis of other similar types of spectroscopy data.
