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The high beam current and sub-angstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopic (EELS) mapping with atomic resolution. These spectral maps are often dose-limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map one can improve background estimation and increase chemical sensitivity. We consider two such approaches- linear combination of power laws and local background averaging-that reduce background error and improve signal extraction. Principal components analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data is PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.
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Atomically resolved electron energy-loss spectroscopy experiments are commonplace in modern aberrationcorrected transmission electron microscopes. Energy resolution has also been increasing steadily with the continuous improvement of electron monochromators. Electronic excitations however are known to be delocalised due to the long range interaction of the charged accelerated electrons with the electrons in a sample. This has made several scientists question the value of combined high spatial and energy resolution for mapping interband transitions and possibly phonon excitation in crystals. In this paper we demonstrate experimentally that atomic resolution information is indeed available at very low energy losses around 100 meV expressed as a modulation of the broadening of the zero loss peak. Careful data analysis allows us to get a glimpse of what are likely phonon excitations with both an energy loss and gain part. These experiments confirm recent theoretical predictions on the strong localisation of phonon excitations as opposed to electronic excitations and show that a combination of atomic resolution and recent developments in increased energy resolution will offer great benefit for mapping phonon modes in real space.
Scientists are drawn to synchrotrons and accelerator based light sources because of their brightness, coherence and flux. The rate of improvement in brightness and detector technology has outpaced Moores law growth seen for computers, networks, and storage, and is enabling novel observations and discoveries with faster frame rates, larger fields of view, higher resolution, and higher dimensionality. Here we present an integrated software/algorithmic framework designed to capitalize on high throughput experiments, and describe the streamlined processing pipeline of ptychography data analysis. The pipeline provides throughput, compression, and resolution as well as rapid feedback to the microscope operators.
Several visualization schemes have been developed for imaging materials at the atomic level through atom probe tomography. The main shortcoming of these tools is their inability to parallel process data using multi-core computing units to tackle the problem of larger data sets. This critically handicaps the ability to make a quantitative interpretation of spatial correlations in chemical composition, since a significant amount of the data is missed during subsequent analysis. In addition, since these visualization tools are not open-source software there is always a problem with developing a common language for the interpretation of data. In this contribution we present results of our work on using an open-source advanced interactive visualization software tool, which overcomes the difficulty of visualizing larger data sets by supporting parallel rendering on a graphical user interface or script user interface and permits quantitative analysis of atom probe tomography data in real time. This advancement allows materials scientists a codesign approach to making, measuring and modeling new and nanostructured materials by providing a direct feedback to the fabrication and designing of samples in real time.
POLAR is a compact space-borne detector initially designed to measure the polarization of hard X-rays emitted from Gamma-Ray Bursts in the energy range 50-500keV. This instrument was launched successfully onboard the Chinese space laboratory Tiangong-2 (TG-2) on 2016 September 15. After being switched on a few days later, tens of gigabytes of raw detection data were produced in-orbit by POLAR and transferred to the ground every day. Before the launch date, a full pipeline and related software were designed and developed for the purpose of quickly pre-processing all the raw data from POLAR, which include both science data and engineering data, then to generate the high level scientific data products that are suitable for later science analysis. This pipeline has been successfully applied for use by the POLAR Science Data Center in the Institute of High Energy Physics (IHEP) after POLAR was launched and switched on. A detailed introduction to the pipeline and some of the core relevant algorithms are presented in this paper.
We study, using simulated experiments inspired by thin film magnetic domain patterns, the feasibility of phase retrieval in X-ray diffractive imaging in the presence of intrinsic charge scattering given only photon-shot-noise limited diffraction data. We detail a reconstruction algorithm to recover the samples magnetization distribution under such conditions, and compare its performance with that of Fourier transform holography. Concerning the design of future experiments, we also chart out the reconstruction limits of diffractive imaging when photon- shot-noise and the intensity of charge scattering noise are independently varied. This work is directly relevant to the time-resolved imaging of magnetic dynamics using coherent and ultrafast radiation from X-ray free electron lasers and also to broader classes of diffractive imaging experiments which suffer noisy data, missing data or both.
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