Gruner, Sol M. | Engineering Papers

DOE OSTI2023

Considerations about future hard x-ray area detectors

X-ray sources continue to advance in both intensity and temporal domains, thereby opening new ways to analyze the structure and properties of matter, provided that the resultant x-ray images can be efficiently and quantitatively recorded. In this perspective we focus on specific limitations of pixel area x-ray detectors. Although pixel area x-ray detectors have also advanced in recent years, many experiments are still detector limited. Specifically, there is need for detectors that can acquire successive images at GHz rates; detectors that can accurately measure both single photon and millions of photons per pixel in the same image at frame rates of hundreds of kHz; and detectors that efficiently capture images of very hard x-rays (20 keV to several hundred keV). The data volumes and data rates of state-of-the-art detection exceeds most practical data storage options and readout bandwidths, thereby necessitating on-line processing of data prior to, or in lieu of full frame readouts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗