The CirPAD, a circular 1.4 M hybrid pixel detector dedicated to X-ray diffraction measurements at Synchrotron SOLEIL

One of the challenges of all synchrotron facilities is to offer the highest performance detectors for all their specific experiments, in particular for X-ray diffraction imaging and its high throughput data collection. In that context, the DiffAbs beamline, the Detectors and the Design and Engineering groups at Synchrotron SOLEIL, in collaboration with ImXPAD and Cegitek companies, have developed an original and unique detector with a circular shape. This detector is based on the hybrid pixel photon-counting technology and consists of the specific assembly of 20 hybrid pixel array detector (XPAD) modules. This article aims to demonstrate the main characteristics of the CirPAD (for Circular Pixel Array Detector) and its performance – i.e. excellent pixel quality, flat-field correction, high-count-rate performance, etc. Additionally, the powder X-ray diffraction pattern of an LaB6 reference sample is presented and refined. The obtained results demonstrate the high quality of the data recorded from the CirPAD, which allows the proposal of its use to all scientific communities interested in performing experiments at the DiffAbs beamline.

Recently Discovered Thick Bentonite Bed Hosted by the Lithothamnium Limestones (Badenian) in the Polish Part of the Carpathian Foredeep: The Evidence for Volcanic Origin

In this paper, we discuss the hypothesis on the volcanic origin of the precursor sediments for a thick (0.6 m) clay bed, hosted by the sequence of lithothamnium limestones of the Pińczów Formation. Combined X-ray powder diffraction, imaging methods (optical and electron microscopy), and chemical analysis were used to document the volcanic markers, which were preserved in the rock studied. The results obtained show that the clay bed discussed is bentonite in origin. This bentonite, which can be called Drugnia Rządowa bentonite, is composed almost entirely of montmorillonite with little admixtures of quartz and biotite. A small amount of calcite is present, but only in the top of the bed. Despite that, the bentonite contains nothing but clay material—it is a model example of entirely altered pyroclastic rock, which retains texture originally developed in volcanic glass fragments and reveals the preserved original features of the precursor fallout pyroclastic deposits (rhyolitic in character). The thick bentonite beds, discovered for the first time within the Badenian lithothamnium limestones of the Pińczów Formation, can be considered as a record of a violent, explosive volcanic event related to the closure of the Outer Carpathian basin and the development of the Carpathian Foredeep.

Skopi: a simulation package for diffractive imaging of noncrystalline biomolecules

X-ray free electron lasers (XFEL) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL experiments involving aerosolized or solvated biomolecular samples offer new challenges both in terms of experimental design and data processing. Skopi is a simulation package that can generate single-hit diffraction images for reconstruction algorithms, multi-hit diffraction images of aggregated particles for training machine learning classification tasks using labeled data, diffraction images of randomly distributed particles for fluctuation X-ray scattering (FXS) algorithms, and diffraction images of reference and target particles for holographic reconstruction algorithms. We envision skopi as a resource to aid the development of on-the-fly feedback during non-crystalline experiments at XFEL facilities, which will provide critical insights into biomolecular structure and function.

Extraction of diffractions from seismic data using convolutional U-net and transfer learning

Diffraction images can be used for modeling reservoir heterogeneities at or below the seismic wavelength scale. However, the extraction of diffractions is challenging because their amplitude is weaker than that of overlapping reflections. Recently, deep learning (DL) approaches have been used as a powerful tool for diffraction extraction. Most DL approaches use a classification algorithm that classifies pixels in the seismic data as diffraction, reflection, noise, or diffraction with reflection, and takes whole values for the classified diffraction pixels. Thus, these DL methods cannot extract diffraction energy from pixels for which diffractions are masked by reflections. We proposed a DL-based diffraction extraction method that preserves the amplitude and phase characteristics of diffractions. Through the systematic generation of a training dataset using synthetic modeling based on t-distributed stochastic neighbor embedding (t-SNE) analysis, this technique extracts not only faint diffractions, but also diffraction tails overlapped by strong reflection events. We also demonstrated that the DL model pre-trained with basic synthetic dataset can be applied to seismic field data through transfer learning. Because the diffractions extracted by our method preserve the amplitude and phase, they can be used for velocity model building and high-resolution diffraction imaging.

Machine learning for laser-induced electron diffraction imaging of molecular structures

Ultrafast diffraction imaging is a powerful tool to retrieve the geometric structure of gas-phase molecules with combined picometre spatial and attosecond temporal resolution. However, structural retrieval becomes progressively difficult with increasing structural complexity, given that a global extremum must be found in a multi-dimensional solution space. Worse, pre-calculating many thousands of molecular configurations for all orientations becomes simply intractable. As a remedy, here, we propose a machine learning algorithm with a convolutional neural network which can be trained with a limited set of molecular configurations. We demonstrate structural retrieval of a complex and large molecule, Fenchone (C10H16O), from laser-induced electron diffraction (LIED) data without fitting algorithms or ab initio calculations. Retrieval of such a large molecular structure is not possible with other variants of LIED or ultrafast electron diffraction. Combining electron diffraction with machine learning presents new opportunities to image complex and larger molecules in static and time-resolved studies.