Pairing glue in cuprate superconductors from the self-energy revealed via machine learning
Pairing glue in cuprate superconductors from the self-energy revealed via machine learning
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DOI：
10.1103/physrevb.101.180510
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发表时间：
2020-05-26
影响因子：
3.7
通讯作者：
Schmalian, Joerg
中科院分区：
文献类型：
--
作者：
Chubukov, Andrey, V;Schmalian, Joerg
最近，机器学习被应用于从Bi基铜氧化物高温超导体的反节点处的光电发射数据中提取自能的正常和异常分量[Y. Yamaji等人，arXiv：1903.08060]。有人认为，这两个分量确实在50 meV附近显示出突出的峰，这包含了关于配对胶的信息，但这些峰隐藏在实际数据中，只测量总的自能。我们在一个有效的费米子玻色子理论中分析了自能。我们表明，软热波动引起的峰值在两个组件的自能在频率可比的超导间隙，而他们取消在总的自能，所有的配对玻色子的性质无关。然而，在量子极限T -> 0突出的峰只存活非常有限的配对相互作用的子类。我们认为，潜在的钉下配对玻色子的方式是确定峰的热演化。
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.