Electron Ghost Imaging

Li, S.; Cropp, Frederick; Kabra, K.; Lane, Thomas J; Wetzstein, Gordon; Musumeci, P.; Ratner, Daniel

doi:10.1103/physrevlett.121.114801

Cited by 113 publications

(62 citation statements)

References 37 publications

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“…321) The deep neural network used here is a tool for classifying the phase and is regarded as a blackbox. The properties of the neural network themselves are also interesting to study from the physics view point , 187, especially in relation to the renormalization group [354][355][356][357][358][359][360][361][362] and tensor network. [363][364][365][366][367][368][369][370][371][372] The vulnerability of phase determination against adversarial perturbation is also an interesting topic.…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions

Ohtsuki

Mano

2020

J. Phys. Soc. Jpn.

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Applications of neural networks to condensed matter physics are becoming popular and beginning to be well accepted. Obtaining and representing the ground and excited state wave functions are examples of such applications. Another application is analyzing the wave functions and determining their quantum phases. Here, we review the recent progress of using the multilayer convolutional neural network, so-called deep learning, to determine the quantum phases in random electron systems. After training the neural network by the supervised learning of wave functions in restricted parameter regions in known phases, the neural networks can determine the phases of the wave functions in wide parameter regions in unknown phases; hence, the phase diagrams are obtained. We demonstrate the validity and generality of this method by drawing the phase diagrams of two-and higher dimensional Anderson metal-insulator transitions and quantum percolations as well as disordered topological systems such as three-dimensional topological insulators and Weyl semimetals. Both real-space and Fourier space wave functions are analyzed. The advantages and disadvantages over conventional methods are discussed. * ohtsuki@sophia.ac.jp

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Section: Summary and Concluding Remarksmentioning

confidence: 99%

Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions

Ohtsuki

Mano

2020

J. Phys. Soc. Jpn.

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“…There has been recent interest in the use of multiplex sensing techniques in applications at the atomic scale [1][2][3][4][5][6][7][8]. Traditionally, measurements using x-rays or electrons systematically interrogate a system at a single point in space, time, or photon/electron energy at once before moving on to the next measurement.…”

Section: Introductionmentioning

confidence: 99%

“…Entanglement has been focus of specific studies in ghost imaging -for perspective see [11] -but is not heavily used in current applications. While the idea to use multiplexing in x-ray and electron experiments has only recently started to recieve significant attention, demonstrations at synchrotrons [1][2][3], FELs [7], and electron sources [6] have already been conducted, paving the way for multiplexing to start to impact applied x-ray and electron science.…”

Section: Introductionmentioning

confidence: 99%

What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging

Lane¹,

Ratner²

2020

Opt. Express

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Ghost imaging, Fourier transform spectroscopy, and the newly developed Hadamard transform crystallography are all examples of multiplexing measurement strategies. Multiplexed experiments are performed by measuring multiple points in space, time, or energy simultaneously. This contrasts to the usual method of systematically scanning single points. How do multiplexed measurements work and when they are advantageous? Here we address these questions with a focus on applications involving x-rays or electrons. We present a quantitative framework for analyzing the expected error and radiation dose of different measurement scheme that enables comparison. We conclude that in very specific situations, multiplexing can offer improvements in resolution and signal-to-noise. If the signal has a sparse representation, these advantages become more general and dramatic, and further less radiation can be used to complete a measurement.

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“…The use of dynamical and digitally controllable laser shaping techniques for the drive laser has been demonstrated in photoinjectors, as in Refs. [10,18,19]. In our case, the DMD enables quantitative comparison between the traditional scanning method and our proposed ghost imaging method.…”

Section: Experimental Demonstrationmentioning

confidence: 98%

“…Implicit in this is the assumption that the spatial profile of the incident illumination varies shot-toshot so that measurements are sufficiently independent. Classical ghost imaging in the spatial domain has been demonstrated experimentally with various sources of illumination, including visible light, optical lasers, x-rays, atoms, and electrons [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Mapping photocathode quantum efficiency with ghost imaging

Kabra

Cropp

et al. 2020

Phys. Rev. Accel. Beams

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Measuring the quantum efficiency (QE) map of a photocathode injector typically requires laser scanning, an invasive operation that involves modifying the injector laser focus and rastering the focused laser spot across the photocathode surface. Raster scanning interrupts normal operation and takes considerable time to setup. In this paper, we demonstrate a novel method of measuring the QE map using a ghost imaging framework that correlates the injector laser spatial variation over time with the total charge yield. Ghost imaging enables passive, real-time monitoring of the QE map without manually modifying the injector laser or interrupting injector operation. We first demonstrate the method at the UCLA Pegasus photoinjector with the help of a digital micromirror device (DMD) and a piezoelectric mirror to increase our control of the overall transverse variance of the illumination profile. The reconstruction algorithm parameters are fine-tuned using simulations and the results are validated against the ground truth map acquired using the traditional rastering method. Finally, we apply the technique to data acquired parasitically from the LCLS photoinjector, showing the feasibility of this method to retrieve a QE map without interrupting normal operation.

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Electron Ghost Imaging

Cited by 113 publications

References 37 publications

Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions

Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions

What are the advantages of ghost imaging? Multiplexing for x-ray and electron imaging

Mapping photocathode quantum efficiency with ghost imaging

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