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A high-order regularized delta-Chebyshev method for computing spectral densities

Computational Physics 2025-12-04 v1 Disordered Systems and Neural Networks Strongly Correlated Electrons

Abstract

We introduce a numerical method for computing spectral densities, and apply it to the evaluation of the local density of states (LDOS) of sparse Hamiltonians derived from tight-binding models. The approach, which we call the high-order delta-Chebyshev method, can be viewed as a variant of the popular regularized Chebyshev kernel polynomial method (KPM), but it uses a high-order accurate approximation of the δ\delta-function to achieve rapid convergence to the thermodynamic limit for smooth spectral densities. The costly computational steps are identical to those for KPM, with high-order accuracy achieved by an inexpensive post-processing procedure. We apply the algorithm to tight-binding models of graphene and twisted bilayer graphene, demonstrating high-order convergence to the LDOS at non-singular points.

Keywords

Cite

@article{arxiv.2512.03149,
  title  = {A high-order regularized delta-Chebyshev method for computing spectral densities},
  author = {Jinjing Yi and Daniel Massatt and Andrew Horning and Mitchell Luskin and J. H. Pixley and Jason Kaye},
  journal= {arXiv preprint arXiv:2512.03149},
  year   = {2025}
}
R2 v1 2026-07-01T08:06:25.300Z