Synchrotron X-ray Data Reconstruction with Deep Neural Networks

• Xiaogang Yang (Brookhaven National Laboratory)

**Abstract**: Data reconstruction is the key step to interpreting the measurement signal into structural information in synchrotron X-ray experiments. It is always challenging due to the complexity of the measurement modalities and data conditions. I will present my developments in using deep neural networks for data reconstruction in synchrotron X-ray measurements. These works are model-based learning, in which no training data and training process are required. My first development case is a 2D image reconstruction algorithm GANrec (Generative Adversarial Network reconstruction algorithm). It was applied to X-ray tomographic reconstruction and holographic phase retrieval. It showed improved reconstruction quality and accuracy for extreme data conditions. The other case is a 1D signal reconstruction process with 1D CNN (Convolutional Neural Network) inverse solver. It was tested for coded-aperture data reconstruction. The results showed less noise and reduced error compared with the classical methods. **Bio**: Xiaogang Yang is a computational scientist at DSSI of NSLS-II since 2021. From 2017 to 2021, he was a scientist working at Petra III of DESY. He did his postdoc in APS of Argonne National Laboratory from 2015 to 2017. He completed his PhD at Delft University of Technology in 2015. His work is mainly on data processing and analysis for synchrotron X-ray measurements. His research specifically focuses on deep learning algorithms for synchrotron X-ray imaging.