Kavli Affiliate: Sol M. Gruner
| First 5 Authors: Zhehui Wang, Andrew F. T. Leong, Angelo Dragone, Arianna E. Gleason, Rafael Ballabriga
| Summary:
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.
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