arXiv : Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

Wang, Zhehui; Xu, Mingwei; Dattelbaum, Dana M.; Rack, Alexander; Strehlow, Joseph; Ballabriga, Rafael; Demarteau, Marcel; Gruner, Sol M.; Da Vià, Cinzia; Fabris, Lorenzo; Xiao, Tiqiao; Fossum, Eric R.; Yue, Xin; Leong, Andrew F.T.; Gleason, Arianna E.; Clark, Samuel J.; Fezzaa, Kamel; Thom-Levy, Julia; Campbell, Michael; Campbell, Christopher; Wang, Feixiang; Dragone, Angelo; Ju, Xiaolu; Llopart, Xavier; Lukić, Bratislav; Therrien, Audrey C.; Hufnagel, Todd C.; Li, Ke

doi:10.1016/j.nima.2023.168690

Article
Report number	arXiv:2308.10643 ; LA-UR-23-29338
Title	Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications
Author(s)	Wang, Zhehui (Los Alamos) ; Leong, Andrew F.T. (Los Alamos) ; Dragone, Angelo (SLAC) ; Gleason, Arianna E. (SLAC) ; Ballabriga, Rafael (CERN) ; Campbell, Christopher (Los Alamos) ; Campbell, Michael (CERN) ; Clark, Samuel J. ; Da Vià, Cinzia (Manchester U.) ; Dattelbaum, Dana M. (Los Alamos) ; Demarteau, Marcel (Oak Ridge) ; Fabris, Lorenzo (Oak Ridge) ; Fezzaa, Kamel ; Fossum, Eric R. (Dartmouth Coll.) ; Gruner, Sol M. (Cornell U.) ; Hufnagel, Todd C. (Johns Hopkins U.) ; Ju, Xiaolu (CAS, SARI, Shanghai) ; Li, Ke (CAS, SARI, Shanghai) ; Llopart, Xavier (CERN) ; Lukić, Bratislav (ESRF, Grenoble) ; Rack, Alexander (ESRF, Grenoble) ; Strehlow, Joseph (Los Alamos) ; Therrien, Audrey C. (Sherbrooke U.) ; Thom-Levy, Julia (Cornell U.) ; Wang, Feixiang (CAS, SARI, Shanghai) ; Xiao, Tiqiao (CAS, SARI, Shanghai) ; Xu, Mingwei (CAS, SARI, Shanghai) ; Yue, Xin (Dartmouth Coll.)
Publication	2023-09-22
Imprint	2023-08-21
Number of pages	36
In:	Nucl. Instrum. Methods Phys. Res., A 1057 (2023) 168690
In:	Ultrafast Imaging and Tracking Instrumentation, Methods and Applications Conference (ULITIMA 2023), SLAC, United States, 13 - 16 Mar 2023, pp.168690
DOI	10.1016/j.nima.2023.168690 (publication)
Subject category	physics.app-ph ; physics.ins-det ; Detectors and Experimental Techniques
Abstract	Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, 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; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. 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.
Copyright/License	preprint: (License: CC BY-NC-ND 4.0) publication: © 2023-2024 The Author(s)