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首页> 外文期刊>Physical review >Pairing glue in cuprate superconductors from the self-energy revealed via machine learning
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Pairing glue in cuprate superconductors from the self-energy revealed via machine learning

机译：通过机器学习的自我能量揭示了铜替代超导体中的胶水

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摘要

Recently, machine learning was applied to extract both the normal and the anomalous components of the self-energy from photoemission data at the antinodal points in Bi- based cuprate high-temperature superconductors [Y. Yamaji et al., arXiv: 1903.08060]. It was argued that both components do show prominent peaks near 50 meV, which hold information about the pairing glue, but the peaks are hidden in the actual data, which measure only the total self-energy. We analyze the self-energy within an effective fermion-boson theory. We show that soft thermal fluctuations give rise to peaks in both components of the self-energy at a frequency comparable to the superconducting gap. while they cancel in the total self-energy, all irrespective of the nature of the pairing boson. However, in the quantum limit T → 0 prominent peaks survive only for a very restricted subclass of pairing interactions. We argue that the way to potentially nail down the pairing boson is to determine the thermal evolution of the peaks.
机译：最近，应用机器学习以在基于Bi基铜高温超导体中的抗透视点的光透射点中提取自我能量的正常和异常组件[Y. Yamaji等人，Arxiv：1903.08060]。有人认为，两种组件都表明了50 MeV附近的突出峰值，该峰值保持了有关配对胶水的信息，但峰值隐藏在实际数据中，该数据仅测量总自能。我们分析了有效的Fermion-Boson理论内的自我能量。我们表明，在与超导间隙相当的频率上，软热波动导致自能的两个部件中的峰值。虽然他们取消了全部自我能量，但无论配对玻色子的性质如何。然而，在量子极限下→0突出的峰值仅在配对交互的非常有限的子类中存活。我们认为，潜在地钉在配对玻色子上的方式是确定峰的热量演变。

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  • 来源
    《Physical review》 |2020年第18期|180510.1-180510.6|共6页
  • 作者

    Andrey V. Chubukov; Joerg Schmalian;

  • 作者单位

    School of Physics and Astronomy and William I. Fine Theoretical Physics Institute University of Minnesota Minneapolis Minnesota 55455 USA;

    Institute for Theory of Condensed Matter Karlsruhe Institute of Technology 76131 Karlsruhe Germany Institute for Quantum Materials and Technologies Karlsruhe Institute of Technology 76021 Karlsruhe Germany;

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