标题： 通过机器学习解耦超导体光电子能谱中的相干和非相干效应 显示英文标题

标题： Disentangling Coherent and Incoherent Effects in Superconductor Photoemission Spectra via Machine Learning

摘要： 解耦强关联材料光电子能谱中的相干和非相干效应通常是一个具有挑战性的问题，因为涉及许多参数。 在本研究中，我们采用机器学习技术，特别是卷积神经网络（CNNs），来解决超导铜氧化物中的双层分裂这一长期问题。 我们展示了在建模谱线上进行CNN训练的有效性，并确认了早期的研究结果，这些结果确立了在整个掺杂范围内存在双层分裂。 此外，我们表明分裂的大小不会随着欠掺杂而减小，这与预期相反。 这种方法不仅突显了机器学习在解决复杂物理问题方面的潜力，还为推进关联超导体电子性质的分析提供了一个稳健的框架。 显示英文摘要

摘要： Disentangling coherent and incoherent effects in the photoemission spectra of strongly correlated materials is generally a challenging problem due to the involvement of numerous parameters. In this study, we employ machine learning techniques, specifically Convolutional Neural Networks (CNNs), to address the long-standing issue of the bilayer splitting in superconducting cuprates. We demonstrate the effectiveness of CNN training on modeled spectra and confirm earlier findings that establish the presence of bilayer splitting across the entire doping range. Furthermore, we show that the magnitude of the splitting does not decrease with underdoping, contrary to expectations. This approach not only highlights the potential of machine learning in tackling complex physical problems but also provides a robust framework for advancing the analysis of electronic properties in correlated superconductors.