论文标题
通过机器学习揭示的来自自能源的Cuprate超导体中的配对胶
The pairing glue in cuprate superconductors from the self-energy revealed via machine learning
论文作者
论文摘要
最近，使用机器学习来从BI基丘陵高温高温超导体中的抗焦点点的光发射数据中提取自我能量的正常和异常组件[Y. yamaji {\ it等人}，arxiv：1903.08060]。有人认为，这两个组件的确显示出附近50 MEV的突出峰，这些峰具有有关配对胶的信息，但是峰隐藏在实际数据中，仅测量总自动能源。我们分析了有效的费米恩 - 玻色孔理论中的自我能源。我们表明，软热波动在自我能源的两个组成部分的峰值上均与超导间隙相当，而它们在总自我能源中取消。无论配对玻色子的性质如何，一切。但是，在量子限制中，$ t \ to 0 $突出峰仅在配对相互作用的非常有限的子类中生存。我们认为，将玻色子钉钉的方法可能是确定峰的热演化。
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 {\it 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 superconducting gap, while they cancel in the total self-energy; all irrespective of the nature of the pairing boson. However, in the quantum limit $T \to 0$ 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.
