002869745 001__ 2869745
002869745 005__ 20241112040401.0
002869745 0248_ $$aoai:cds.cern.ch:2869745$$pcerncds:FULLTEXT$$pcerncds:CERN:FULLTEXT$$pcerncds:CERN
002869745 0247_ $$2DOI$$9arXiv$$a10.1016/j.nima.2023.168690$$qpublication
002869745 037__ $$9arXiv$$aarXiv:2308.10643$$cphysics.ins-det
002869745 037__ $$9arXiv:reportnumber
002869745 037__ $$aLA-UR-23-29338
002869745 035__ $$9arXiv$$aoai:arXiv.org:2308.10643
002869745 035__ $$9Inspire$$aoai:inspirehep.net:2689927$$d2024-11-11T16:07:17Z$$h2024-11-12T03:00:02Z$$mmarcxml$$ttrue$$uhttps://inspirehep.net/api/oai2d
002869745 035__ $$9Inspire$$a2689927
002869745 041__ $$aeng
002869745 100__ $$aWang, [email protected]$$uLos Alamos$$vLos Alamos National Laboratory, Los Alamos, NM 87545, USA
002869745 245__ $$9arXiv$$aUltrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications
002869745 269__ $$c2023-08-21
002869745 260__ $$c2023-09-22
002869745 300__ $$a36 p
002869745 520__ $$9Elsevier B.V.$$aUltrafast 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.
002869745 520__ $$9arXiv$$aUltrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification, and U-RadIT optimization.
002869745 540__ $$3preprint$$aCC BY-NC-ND 4.0$$uhttp://creativecommons.org/licenses/by-nc-nd/4.0/
002869745 542__ $$3publication$$dThe Author(s)$$g2023
002869745 595__ $$cHAL
002869745 65017 $$2arXiv$$aphysics.app-ph
002869745 65017 $$2arXiv$$aphysics.ins-det
002869745 65017 $$2SzGeCERN$$aDetectors and Experimental Techniques
002869745 690C_ $$aCERN
002869745 690C_ $$aARTICLE
002869745 700__ $$aLeong, Andrew F.T.$$uLos Alamos$$vLos Alamos National Laboratory, Los Alamos, NM 87545, USA
002869745 700__ $$aDragone, Angelo$$uSLAC$$vSLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
002869745 700__ $$aGleason, Arianna E.$$uSLAC$$vSLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
002869745 700__ $$aBallabriga, Rafael$$uCERN$$vCERN, 1211 Geneva 23, Switzerland
002869745 700__ $$aCampbell, Christopher$$uLos Alamos$$vLos Alamos National Laboratory, Los Alamos, NM 87545, USA
002869745 700__ $$aCampbell, Michael$$uCERN$$vCERN, 1211 Geneva 23, Switzerland
002869745 700__ $$aClark, Samuel J.$$vX-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
002869745 700__ $$aDa Vià, Cinzia$$uManchester U.$$vThe University of Manchester, Manchester, M13 9PL, UK
002869745 700__ $$aDattelbaum, Dana M.$$uLos Alamos$$vLos Alamos National Laboratory, Los Alamos, NM 87545, USA
002869745 700__ $$aDemarteau, Marcel$$uOak Ridge$$vOak Ridge National Laboratory, Oak Ridge, TN 37831, USA
002869745 700__ $$aFabris, Lorenzo$$uOak Ridge$$vOak Ridge National Laboratory, Oak Ridge, TN 37831, USA
002869745 700__ $$aFezzaa, Kamel$$vX-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
002869745 700__ $$aFossum, Eric R.$$uDartmouth Coll.$$vThayer school of engineering at Dartmouth, Dartmouth college, Hanover, NH 03755, USA
002869745 700__ $$aGruner, Sol M.$$uCornell U.$$vDepartment of Physics, Cornell University, Ithaca, NY 14853, USA
002869745 700__ $$aHufnagel, Todd C.$$uJohns Hopkins U.$$vDepartment of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
002869745 700__ $$aJu, Xiaolu$$uCAS, SARI, Shanghai$$vShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
002869745 700__ $$aLi, Ke$$uCAS, SARI, Shanghai$$vShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
002869745 700__ $$aLlopart, Xavier$$uCERN$$vCERN, 1211 Geneva 23, Switzerland
002869745 700__ $$aLukić, Bratislav$$uESRF, Grenoble$$vESRF – The European Synchrotron, 71 avenue des Martyrs - CS 40220, 38043 Grenoble Cedex 9, France
002869745 700__ $$aRack, Alexander$$uESRF, Grenoble$$vESRF – The European Synchrotron, 71 avenue des Martyrs - CS 40220, 38043 Grenoble Cedex 9, France
002869745 700__ $$aStrehlow, Joseph$$uLos Alamos$$vLos Alamos National Laboratory, Los Alamos, NM 87545, USA
002869745 700__ $$aTherrien, Audrey C.$$uSherbrooke U.$$vInterdisciplinary Institute for Technological Innovation, Université de Sherbrooke, 3000 boulevard de l’Université, Sherbrooke, J1K 0A5, Québec, Canada
002869745 700__ $$aThom-Levy, Julia$$uCornell U.$$vDepartment of Physics, Cornell University, Ithaca, NY 14853, USA
002869745 700__ $$aWang, Feixiang$$uCAS, SARI, Shanghai$$vShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
002869745 700__ $$aXiao, Tiqiao$$uCAS, SARI, Shanghai$$vShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
002869745 700__ $$aXu, Mingwei$$uCAS, SARI, Shanghai$$vShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China
002869745 700__ $$aYue, Xin$$uDartmouth Coll.$$vThayer school of engineering at Dartmouth, Dartmouth college, Hanover, NH 03755, USA
002869745 773__ $$c168690$$mpublication$$pNucl. Instrum. Methods Phys. Res., A$$v1057$$xNuclear Inst. and Methods in Physics Research, A 1057 (2023)
168690$$y2023
002869745 8564_ $$82474825$$s9306$$uhttp://cds.cern.ch/record/2869745/files/Strehlow3.png$$y00031 X-ray energy (defined in Figure \ref{fig:StrSS}) vs source size as delivered via a wide range of high intensity, short pulse laser-target configurations. For each source type, a least-squares regression (solid lines) establishes the relationship between energy $E$ and source size $S$. An additional data point for a conventional MeV X-ray source, the 60 ns DARHT Axis I accelerator (triangle)~\cite{Darht:2008,Nath:2010}, is shown for comparison.
002869745 8564_ $$82474826$$s22594041$$uhttp://cds.cern.ch/record/2869745/files/2308.10643.pdf$$yFulltext
002869745 8564_ $$82474827$$s240228$$uhttp://cds.cern.ch/record/2869745/files/Camp1.png$$y00003 A 120GeV/c muon track which produces a delta electron reconstructed from the ToT and ToA information provided by a Timepix3 chip~\cite{Camp:8}. The colors and diameters of the points represent the charge detected in that voxel.
002869745 8564_ $$82474828$$s1101161$$uhttp://cds.cern.ch/record/2869745/files/Figure1c.png$$y00001 A simplified schematic U-RadIT setup consisting of a radiation source, a target that interacts with a driver (energy source), and the detector. Mechanical, kinetic, chemical, electric, and electromagnetic (including lasers) energy are possible sources of driver energy.
002869745 8564_ $$82474829$$s405915$$uhttp://cds.cern.ch/record/2869745/files/UFRadITHis3.png$$y00020 Evolution of high-speed imaging frame rate, including high-speed X-ray imaging. The large blue dots in the upper right corner corresponds to high frame rates reached by using different compressed sensing methods.
002869745 8564_ $$82474830$$s2178349$$uhttp://cds.cern.ch/record/2869745/files/Camp3.png$$y00004 Left: histogram showing the number of pixels for a given noise in e- rms. Right: the geographical distribution of noise across the ASIC. Pixels in regions above the peripheral and control logic have slightly elevated noise values ($\sim$10 $e^-$ rms more) because of the shielding added to prevent the injection of digital noise.
002869745 8564_ $$82474831$$s465151$$uhttp://cds.cern.ch/record/2869745/files/Tammy1.png$$y00029 An example of the reduction of fill-tube hole diameter with time in NIF experiments~\cite{Ma:2023}. The smallest fill-tube design was deployed in the ignited NIF experiments. Image credit: General Atomics (GA) and Lawrence Livermore National Laboratory (LLNL).
002869745 8564_ $$82474832$$s1265608$$uhttp://cds.cern.ch/record/2869745/files/OptimizationLoops2.png$$y00002 A holistic framework to optimize U-RadIT information yield includes two correlated loops: The hardware loop on the left is driven by signal optimization, which may include the radiation source (low emittance, coherence, adjustable spectrum), the detectors, and detection methods. The data or `digital twin' loop on the right is driven by computation towards interpretable synthetic data that can be directly compared with experimental data including images.
002869745 8564_ $$82474833$$s370053$$uhttp://cds.cern.ch/record/2869745/files/Dana1.png$$y00026 Pre-shock (static) and 7 dynamic frames from x-ray phase contrast imaging of a 3rd-order Menger structure during shockwave loading following projectile impact at 331( or 318?) m/s (Shot 19-2-017). The frames are timed to the 24-bunch mode of the Advanced Photon Source. In the phase contrast images, the shock travels from the baseplate (right side, no X-ray transmission) into the structure, resulting in substantial lateral displacement and dissipation of the shock. Reprinted from~\cite{D1}, with the permission of AIP Publishing.
002869745 8564_ $$82474834$$s501161$$uhttp://cds.cern.ch/record/2869745/files/XPCIhistory.png$$y00017 Evolution of XPCI modalities enabled by the advances in X-ray sources. Three popular modalities of XPCI: a.) Grating-based interferometric method; b.) Speckle-based XPCI; and c.) propagation-based XPCI are highlighted in the upper left corner.
002869745 8564_ $$82474835$$s166992$$uhttp://cds.cern.ch/record/2869745/files/ESRFfigure1.png$$y00022 Series of radiography images showing shock-cavity interactions: (a) 4~mm cavity with 8.63~GPa shock, (b) 6~mm cavity with 12.80~GPa shock, (c) 6~mm cavity with 16.60~GPa shock imaged with 3.8~million FPS. The insets in (c) show the rear-surface optical images of the toroidal plasma emission. Reproduced from \cite{escauriza2020}. CC BY 4.0.
002869745 8564_ $$82474836$$s33609$$uhttp://cds.cern.ch/record/2869745/files/di_fig2.png$$y00018 In dark-field x-ray microscopy an objective is placed in the diffracted beam to provide a magnified view of a crystal grain. (Adapted from Ref.~\cite{PoulsenCOSSMS:20}.)
002869745 8564_ $$82474837$$s5747445$$uhttp://cds.cern.ch/record/2869745/files/Campbellv2.png$$y00027 Single frame analysis from shot 20-2-081 using the ground truth approach. Here, the 4th frame is shown, in which shock localization led to "cell" rotation and material extrusion lateral to the shock direction. b) Corner velocity as a function of horizontal position stacked by frame shows a broad velocity disturbance with a maximum of ~250 m/s moving into the structure. The velocity increase is not discontinuous (shock-like), but is spread over nearly 0.5 mm. Figure adopted from~\cite{D2}.
002869745 8564_ $$82474838$$s111123$$uhttp://cds.cern.ch/record/2869745/files/Dart3.png$$y00021 The overall flow of compressive imager from~\cite{Dart:16}.
002869745 8564_ $$82474839$$s67460$$uhttp://cds.cern.ch/record/2869745/files/Ari4.png$$y00028 Schematic of laser-driven shock compression of crystalline phase using simultaneous {\it in situ} imaging and X-ray diffraction. Dynamic X-ray imaging diagnostics at the MEC, LCLS End-station can visualize the shock compression process of crystalline materials phase transforming into a molten state. X-ray imaging and diffraction fidelity can resolve lattice level transformations, provide phase fraction information and data quality suitable for Reitveld refinements and {\it in situ} imaging resolution to 400 nm within 60 femtoseconds. Schematic courtesy of G. Stewart, SLAC Graphic Artist.
002869745 8564_ $$82474840$$s316466$$uhttp://cds.cern.ch/record/2869745/files/Fezzaa1.png$$y00024 Simplified schematic of the detonation experiment. (a) The composite explosives mixture (comp. B) pallet is placed in an ice block that is cut open to recover the detonation products. (b) inset of the different regions of the detonation front that the X-ray beam is probing. Figure from Ref.~\cite{Fez7} reused with permission.
002869745 8564_ $$82474841$$s534936$$uhttp://cds.cern.ch/record/2869745/files/Fezzaa2.png$$y00025 Real-time keyhole porosity detection in LPBF. (A) Schematic of the simultaneous synchrotron x-ray and thermal imaging experiment on scanning laser melting of Ti-6Al-4V. (B) A representative angle top-view thermal image. (C) A representative side-view x-ray image. (D) Typical time-series signal of the average emission intensity from the keyhole region [(B), dashed oval] extracted from the thermal image sequence. (E) Wavelet analysis performed over the time-series signals in (D). The scalogram is sectioned into a few windows, which are then labeled as either ``Non-pore” or ``Pore” on the basis of the operando X-ray imaging result. (F) Machine-learning approach with sectioned scalograms as input data. A CNN was used, which is composed of a series of alternating convolution and pooling layers and a final layer. Each convolution layer extracts features from its previous layer, using filters learned from the trained model, to form a feature map. The feature map is then down-sampled by a pooling layer to reduce the number of parameters to learn. The final layer of the CNN classifies the input scalogram as either ``Non-pore” or ``Pore.” Figure from Ref.~\cite{Fez8} reused with permission.
002869745 8564_ $$82474842$$s121424$$uhttp://cds.cern.ch/record/2869745/files/Dart10.png$$y00013 Schematic of pixel based on FD storage reported in Ref.~\cite{Dart:18}.
002869745 8564_ $$82474843$$s67956$$uhttp://cds.cern.ch/record/2869745/files/SpectralShapesX.png$$y00030 The spectral shapes of the laser-driven sources and their representative energies. Idealized $K_\alpha$ emission is a monoenergetic peak, shown here at 17.5 keV to represent a molybdenum $K_\alpha$ line. The betatron spectrum has a mean energy $E_{avg}\approx$ 100 keV, identified by the dashed line. The bremsstrahlung spectrum is characterized by a single slope, or "temperature," in the MeV range (inset). The fit for the intensity ($I_0$) as a function of energy ($E$) is of the form $I_0 \propto e^{-E/T}$, with temperature $T$ = 2.2 MeV.
002869745 8564_ $$82474844$$s59921$$uhttp://cds.cern.ch/record/2869745/files/Dart8.png$$y00011 High capacitance density vertical capacitor and its cross-section reported in Ref.~\cite{Dart:9}.
002869745 8564_ $$82474845$$s5328405$$uhttp://cds.cern.ch/record/2869745/files/CC1.png$$y00019 MCXI of evolution of a laser-induced cavitation. Where (a) frame No.115 of the original image of cavitation evolution; (b) cavitation contraction and center re-excitation stage, starting frame No.97; (c) movement trend diagram of cavitation contraction stage; (d) cavitation rupture and jet flow stage, starting frame No.133.
002869745 8564_ $$82474846$$s80682$$uhttp://cds.cern.ch/record/2869745/files/ESRFfigure2bis.png$$y00023 Series of phase-contrast radiographs showing the pulsed-power induced planar shock wave from an array of wires, and its interactions at the interface of different density media. Important features are marked within the images. Reprinted from \cite{strucka2023}, with the permission of AIP Publishing.
002869745 8564_ $$82474847$$s82462$$uhttp://cds.cern.ch/record/2869745/files/Dart9.png$$y00012 layout of the CCD memories in pixel reported in Ref.~\cite{Dart:15}.
002869745 8564_ $$82474848$$s197046$$uhttp://cds.cern.ch/record/2869745/files/Dart4.png$$y00007 The layout of the high-speed pixel from Ref.~\cite{Dart:14} (left) and high-speed charge-sweep pixel from Ref.~\cite{Dart:8} (right).
002869745 8564_ $$82474849$$s183444$$uhttp://cds.cern.ch/record/2869745/files/Dart5.png$$y00008 Conceptual layout of the high-speed pixel from Ref.~\cite{Dart:15} (a) and its electrostatic potential diagram (b).
002869745 8564_ $$82474850$$s273089$$uhttp://cds.cern.ch/record/2869745/files/Dart6.png$$y00009 Conceptual layout of the high-speed pixel from Ref.~\cite{Dart:9}.
002869745 8564_ $$82474851$$s93208$$uhttp://cds.cern.ch/record/2869745/files/Dart7.png$$y00010 Two major implementations of the in-pixel readout circuit.
002869745 8564_ $$82474852$$s65418$$uhttp://cds.cern.ch/record/2869745/files/Dart1.png$$y00005 Conceptual operation timing diagram of the rolling shutter image sensor(a), global shutter image sensor(b), and burst-mode image sensor.
002869745 8564_ $$82474853$$s96589$$uhttp://cds.cern.ch/record/2869745/files/Dart2.png$$y00006 Conceptual structure of voltage domain storage burst-mode image sensor(a) and charge domain storage burst-mode image sensor(b).
002869745 8564_ $$82474854$$s338901$$uhttp://cds.cern.ch/record/2869745/files/TimeLengthScale2.png$$y00000 A comparison of various U-RadIT modalities, electrons, X-rays at different energies, and charged particles such as protons, and their applicable temporal and spatial scales. The four sloped lines correspond to, respectively, the sound speed in air ($C_s$(air)), the sound speed in water ($C_s$(H$_2$O)), 10 times the $C_s$(H$_2$O), and the hypervelocity at 300 km/s as in the National Ignition Facility (NIF) experiments.
002869745 8564_ $$82474855$$s171234$$uhttp://cds.cern.ch/record/2869745/files/figL3.png$$y00016 PDC tile concept. A tile controller manages currently up to 64 PDC chips. Note that wire bonds are in the process of being phased out in favor of through-silicon vias to improve fill-factor.
002869745 8564_ $$82474856$$s40046$$uhttp://cds.cern.ch/record/2869745/files/figL2.png$$y00015 conceptual cross-section of a PDC. The top tier is made by using a dedicated optoelectronic process. The bottom tier has all the digital electronics needed to read out each SPAD.
002869745 8564_ $$82474857$$s33681$$uhttp://cds.cern.ch/record/2869745/files/figL1.png$$y00014 Conceptual difference between a SiPM and a PDC. PDCs are intrinsically digital devices and avoid the need for expensive analog processing and digital conversion.
002869745 8564_ $$82484121$$s7027514$$uhttp://cds.cern.ch/record/2869745/files/Publication.pdf$$yFulltext
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