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Single-shot tomographic movies of evolving light-velocity objects

Author

Listed:
  • Zhengyan Li

    (University of Texas at Austin)

  • Rafal Zgadzaj

    (University of Texas at Austin)

  • Xiaoming Wang

    (University of Texas at Austin)

  • Yen-Yu Chang

    (University of Texas at Austin)

  • Michael C. Downer

    (University of Texas at Austin)

Abstract

Tomography—cross-sectional imaging based on measuring radiation transmitted through an object along different directions—enables non-invasive imaging of hidden stationary objects, such as internal bodily organs, from their sequentially measured projections. Here we adapt tomographic methods to visualize—in one laser shot—the instantaneous structure and evolution of a laser-induced object propagating through a transparent Kerr medium. We reconstruct ‘movies’ of a laser pulse’s diffraction, self-focusing and filamentation from phase ‘streaks’ imprinted onto probe pulses that cross the main pulse’s path simultaneously at different angles. Multiple probes are generated and detected compactly and simply, making the system robust, easy to align and adaptable to many problems. Our technique could potentially visualize, for example, plasma wakefield accelerators, optical rogue waves or fast ignitor pulses, light-velocity objects, whose detailed space–time dynamics are known only through intensive computer simulations.

Suggested Citation

  • Zhengyan Li & Rafal Zgadzaj & Xiaoming Wang & Yen-Yu Chang & Michael C. Downer, 2014. "Single-shot tomographic movies of evolving light-velocity objects," Nature Communications, Nature, vol. 5(1), pages 1-12, May.
  • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4085
    DOI: 10.1038/ncomms4085
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    Cited by:

    1. Junliang Dong & Pei You & Alessandro Tomasino & Aycan Yurtsever & Roberto Morandotti, 2023. "Single-shot ultrafast terahertz photography," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    2. Zhoutian Liu & Lele Wang & Yuan Meng & Tiantian He & Sifeng He & Yousi Yang & Liuyue Wang & Jiading Tian & Dan Li & Ping Yan & Mali Gong & Qiang Liu & Qirong Xiao, 2022. "All-fiber high-speed image detection enabled by deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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