Collimator irradiation studies in the Argonne Advanced Photon Source at energy densities expected in next-generation storage ring light sources

Dooling, J. C.; Borland, M.; Berg, William J.; Calvey, Joseph; Decker, G.; Emery, L.; Harkay, K.; Lindberg, Ryan; Navrotksi, G.; Sajaev, V.; Shoaf, S. E.; Sun, Yifeng; Wootton, Kent; Xiao, Aimin; Grannan, A. M.; Lumpkin, A.H.

doi:10.1103/physrevaccelbeams.25.043001

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Figure 1 shows the evolution of the peak data rate due to the increasing X-ray source brilliance over the years [8]. The fourth generation synchrotrons such as APS-U [9] and PETRA IV [10] will have a significant reduction in emittance and a brilliance increase by a factor about 10 3 over the parameters of the third generation synchrotrons such as APS and PETRA III. XFELs, which are many of orders of magnitude brighter than synchrotrons, will run at a higher repetition rate up to 1 MHz [11].…”

Section: Introductionmentioning

confidence: 99%

Neural network methods for radiation detectors and imaging

Lin,

Ning,

Zhu

et al. 2024

Front. Phys.

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Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

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Section: Introductionmentioning

confidence: 99%

Neural network methods for radiation detectors and imaging

Lin,

Ning,

Zhu

et al. 2024

Front. Phys.

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“…The rapid emergence of next-generation X-ray sources, including near diffraction-limited storage rings (DLSRs) such as PETRA IV (Schroer et al, 2018), MAX IV (Bjo ¨rklund Svensson et al, 2019), ESRF-EBS (Chenevier & Joly, 2018) and APS-U (Dooling et al, 2022), as well as free-electron lasers (FELs) such as European XFEL (Tschentscher et al, 2017), LCLS II (Halavanau et al, 2019), SwissFEL (Milne et al, 2017) and SACLA (Yabashi et al, 2017), promises an increase of coherent X-ray flux by several orders of magnitude. Combined with the development of high-speed, highfidelity pixelated photon-counting X-ray detectors (Pennicard et al, 2018;Allahgholi et al, 2019;Ballabriga et al, 2018;Mo ¨ller et al, 2019), XPCS will have the potential to fill the 'noman's land' in S(Q, !)…”

Section: Introductionmentioning

confidence: 99%

Direct measurement of Stokes–Einstein diffusion of Cowpea mosaic virus with 19 µs-resolved XPCS

Switalski¹,

Fan²,

Li³

et al. 2022

J Synchrotron Radiat

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Brownian motion of Cowpea mosaic virus (CPMV) in water was measured using small-angle X-ray photon correlation spectroscopy (SA-XPCS) at 19.2 µs time resolution. It was found that the decorrelation time τ(Q) = 1/DQ 2 up to Q = 0.091 nm−1. The hydrodynamic radius R H determined from XPCS using Stokes–Einstein diffusion D = kT/(6πηR H) is 43% larger than the geometric radius R 0 determined from SAXS in the 0.007 M K3PO4 buffer solution, whereas it is 80% larger for CPMV in 0.5 M NaCl and 104% larger in 0.5 M (NH4)2SO4, a possible effect of aggregation as well as slight variation of the structures of the capsid resulting from the salt–protein interactions.

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