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Register a new userPtychographic wavefront characterisation for single-particle imaging at X-ray lasers
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A well-characterised wavefront is important for many X-ray free-electron laser (XFEL) experiments, especially for single-particle imaging (SPI), where individual bio-molecules randomly sample a nanometer-region of highly-focused femtosecond pulses. We demonstrate high-resolution multiple-plane wavefront imaging of an ensemble of XFEL pulses, focused by Kirkpatrick-Baez (KB) mirrors, based on mixed-state ptychography, an approach letting us infer and reduce experimental sources of instability. From the recovered wavefront profiles, we show that while local photon fluence correction is crucial and possible for SPI, a small diversity of phase-tilts likely has no impact. Our detailed characterisation will aid interpretation of data from past and future SPI experiments, and provides a basis for further improvements to experimental design and reconstruction algorithms.
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An improved analysis for single particle imaging (SPI) experiments, using the limited data, is presented here. Results are based on a study of bacteriophage PR772 performed at the AMO instrument at the Linac Coherent Light Source (LCLS) as part of the SPI initiative. Existing methods were modified to cope with the shortcomings of the experimental data: inaccessibility of information from the half of the detector and small fraction of single hits. General SPI analysis workflow was upgraded with the expectation-maximization based classification of diffraction patterns and mode decomposition on the final virus structure determination step. The presented processing pipeline allowed us to determine the three-dimensional structure of the bacteriophage PR772 without symmetry constraints with a spatial resolution of 6.9 nm. The obtained resolution was limited by the scattering intensity during the experiment and the relatively small number of single hits.
We present a method for the measurement of the phase gradient of a wavefront by tracking the relative motion of speckles in projection holograms as a sample is scanned across the wavefront. By removing the need to obtain an un-distorted reference image of the sample, this method is suitable for the metrology of highly divergent wavefields. Such wavefields allow for large magnification factors, that, according to current imaging capabilities, will allow for nano-radian angular sensitivity and nano-scale sample projection imaging. Both the reconstruction algorithm and the imaging geometry are nearly identical to that of ptychography, except that the sample is placed downstream of the beam focus and that no coherent propagation is explicitly accounted for. Like other x-ray speckle tracking methods, it is robust to low-coherence x-ray sources making is suitable for lab based x-ray sources. Likewise it is robust to errors in the registered sample positions making it suitable for x-ray free-electron laser facilities, where beam pointing fluctuations can be problematic for wavefront metrology. We also present a modified form of the speckle tracking approximation, based on a second-order local expansion of the Fresnel integral. This result extends the validity of the speckle tracking approximation and may be useful for similar approaches in the field.
Obtaining 3D information from a single X-ray exposure at high-brilliance sources, such as X-ray free-electron lasers (XFELs) [1] or diffraction-limited storage rings [2], allows the study of fast dynamical processes in their native environment. However, current X-ray 3D methodologies are either not compatible with single-shot approaches because they rely on multiple exposures, such as confocal microscopy [3, 4] and tomography [5, 6]; or they record a single projection per pulse [7] and are therefore restricted to approximately two-dimensional objects [8]. Here we propose and verify experimentally a novel imaging approach named X-ray multi-projection imaging (XMPI), which simultaneously acquires several projections without rotating the sample at significant tomographic angles. When implemented at high-brilliance sources it can provide volumetric information using a single pulse. Moreover, XMPI at MHz repetition XFELs could allow a way to record 3D movies of deterministic or stochastic natural processes in the micrometer to nanometer resolution range, and at time scales from microseconds down to femtoseconds.
X-ray Absorption Spectroscopy (XAS) is a widely used X-ray diagnostic method. While synchrotrons have large communities of XAS users, its use on X-Ray Free Electron Lasers (XFEL) facilities has been rather limited. At a first glance, the relatively narrow bandwidth and the highly fluctuating spectral structure of XFEL sources seem to prevent high-quality XAS measurements without accumulating over many shots. Here, we demonstrate for the first time the collection of single-shot XAS spectra on an XFEL, with error bars of only a few percent, over tens of eV. We show how this technique can be extended over wider spectral ranges towards Extended X-ray Absorption Fine Structure (EXAFS) measurements, by concatenating a few tens of single-shot measurements. Such results open indisputable perspectives for future femtosecond time resolved XAS studies, especially for transient processes that can be initiated at low repetition rate.
Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.
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