Fast reconstruction of single-shot wide-angle diffraction images through deep learning

Abstract

Single-shot X-ray imaging of short-lived nanostructures such as clusters and nanoparticles near a phase transition or non-crystalizing objects such as large proteins and viruses is currently the most elegant method for characterizing their structure. Using hard X-ray radiation provides scattering images that encode two-dimensional projections, which can be combined to identify the full three-dimensional object structure from multiple identical samples. Wide-angle scattering using XUV or soft X-rays, despite yielding lower resolution, provides three-dimensional structural information in a single shot and has opened routes towards the characterization of non-reproducible objects in the gas phase. The retrieval of the structural information contained in wide-angle scattering images is highly non-trivial, and currently no efficient rigorous algorithm is known. Here we show that deep learning networks, trained with simulated scattering data, allow for fast and accurate reconstruction of shape and orientation of nanoparticles from experimental images. The gain in speed compared to conventional retrieval techniques opens the route for automated structure reconstruction algorithms capable of real-time discrimination and pre-identification of nanostructures in scattering experiments with high repetition rate -- thus representing the enabling technology for fast femtosecond nanocrystallography.

arXiv comment14 pages, 5 figures

References in corpus (15)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Femtosecond Diffractive Imaging with a Soft-X-ray Free-Electron Laser
• Many-body quantum state tomography with neural networks
• Learning phase transitions by confusion
• Discovering Phase Transitions with Unsupervised Learning
• Three-dimensional structure determination from a single view
• Machine learning out-of-equilibrium phases of matter
• Real-time coherent diffraction inversion using deep generative networks
• Coherent diffractive imaging of single helium nanodroplets with a high harmonic generation source
• Femtosecond X-ray Fourier holography imaging of free-flying nanoparticles
• Local-measurement-based quantum state tomography via neural networks
• Three-Dimensional Shapes of Spinning Helium Nanodroplets
• Low-signal limit of X-ray single particle imaging
• Deep neural networks for classifying complex features in diffraction images

Cited by in corpus (2)

• Reconstruction of nanoscale particles from single-shot wide-angle FEL diffractions patterns with physics-informed neural networks
• Reconstruction of polytopes from the modulus of the Fourier transform with small wave length