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Application of the SD technique for solving a BCS-reduced Hubbard like Hamiltonian

机译:SD技术在解决BCS减少的哈伯德像哈密顿方程中的应用

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We present exact and stochastic diagonalization results for a BCS-reduced Hubbard model. The kinetic Hamiltonian is the same as in the single band Hubbard model with additional next nearest neighbor hopping. The interaction of this model is designed to inhibit superconductivity in the d(x2-y2) channel. The ground state of this model is studied by exact and stochastic diagonalization technique. We present a review of the technical details of the application of the stochastic diagonalization algorithm on this problem. To verify our results obtained with the stochastic diagonalization, they are compared with the exact diagonalization results. In order to show the convergence of the stochastic diagonalization we give a detailed analysis of the behavior of physical properties with increasing number of states. Finally we study superconductivity in this BCS-reduced Hubbard model. As an indicator of superconductivity we use the occurrence of Off Diagonal Long Range Order. We study the scaling behavior of this model for various attractive interactions and in addition the dependence of the superconducting correlation functions from the filling of the system.
机译:我们提出了BCS减少的Hubbard模型的精确和随机对角化结果。动力学哈密顿量与单波段哈伯德模型相同,但具有额外的次近邻跳变。该模型的相互作用旨在抑制d(x2-y2)通道中的超导性。通过精确和随机对角化技术研究了该模型的基态。我们目前对随机对角化算法在该问题上的应用技术细节进行综述。为了验证我们用随机对角化获得的结果,将它们与精确的对角化结果进行比较。为了显示随机对角化的收敛,我们对随着状态数量增加的物理特性的行为进行了详细的分析。最后,我们在此BCS简化的Hubbard模型中研究超导性。作为超导性的指标,我们使用了非对角线长程命令的出现。我们研究了该模型对各种有吸引力的相互作用的缩放行为,此外,还研究了系统填充对超导相关函数的依赖性。

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