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A terahertz-driven non-equilibrium phase transition in a room temperature atomic vapour

Author

Listed:
  • C. G. Wade

    (Durham University
    University of Oxford)

  • M. Marcuzzi

    (University of Nottingham
    University of Nottingham)

  • E. Levi

    (University of Nottingham
    University of Nottingham)

  • J. M. Kondo

    (Durham University)

  • I. Lesanovsky

    (University of Nottingham
    University of Nottingham)

  • C. S. Adams

    (Durham University)

  • K. J. Weatherill

    (Durham University)

Abstract

There are few demonstrated examples of phase transitions that may be driven directly by terahertz frequency electric fields, and those that are known require field strengths exceeding 1 MV cm−1. Here we report a non-equilibrium phase transition driven by a weak (≪1 V cm−1), continuous-wave terahertz electric field. The system consists of room temperature caesium vapour under continuous optical excitation to a high-lying Rydberg state, which is resonantly coupled to a nearby level by the terahertz electric field. We use a simple model to understand the underlying physical behaviour, and we demonstrate two protocols to exploit the phase transition as a narrowband terahertz detector: the first with a fast (20 μs) non-linear response to nano-Watts of incident radiation, and the second with a linearised response and effective noise equivalent power ≤1 pW Hz−1/2. The work opens the door to a class of terahertz devices controlled with low-field intensities and operating in a room temperature environment.

Suggested Citation

  • C. G. Wade & M. Marcuzzi & E. Levi & J. M. Kondo & I. Lesanovsky & C. S. Adams & K. J. Weatherill, 2018. "A terahertz-driven non-equilibrium phase transition in a room temperature atomic vapour," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05597-4
    DOI: 10.1038/s41467-018-05597-4
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    Cited by:

    1. Zong-Kai Liu & Li-Hua Zhang & Bang Liu & Zheng-Yuan Zhang & Guang-Can Guo & Dong-Sheng Ding & Bao-Sen Shi, 2022. "Deep learning enhanced Rydberg multifrequency microwave recognition," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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