6 research outputs found
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
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 makes 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.Comment: 51 pages, 31 figures; Overview of ultrafast radiographic imaging and
tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park,
CA, US
Increased speed: 3D silicon sensors; Fast current amplifiers
The authors describe techniques to make fast, sub-nanosecond time resolution solid-state detector systems using sensors with 3D electrodes, current amplifiers, constant-fraction comparators or fast wave-form recorders, and some of the next steps to reach still faster results
Future trends of 3D silicon sensors
Vertex detectors for the next LHC experiments upgrades will need to have low mass while at the same time be radiation hard and with sufficient granularity to fulfil the physics challenges of the next decade. Based on the gained experience with 3D silicon sensors for the ATLAS IBL project and the on-going developments on light materials, interconnectivity and cooling, this paper will discuss possible solutions to these requirements
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.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