Symmetric approximant formalism for statistical topological matter
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866918269575757824 |
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| author | Zijderveld, R. Johanna Chaou, Adam Yanis Day, Isidora Araya Akhmerov, Anton R. |
| author_facet | Zijderveld, R. Johanna Chaou, Adam Yanis Day, Isidora Araya Akhmerov, Anton R. |
| contents | The standard approach to characterizing topological matter, computing topological invariants, fails when the symmetry protecting the topological phase is preserved only on average in a disordered system. Because topological invariants rely on enforcing the symmetry exactly, they can overcount phases by incorrectly identifying certain non-robust features as robust. Moreover, in intrinsic statistical topological insulators, enforcing the symmetry exactly is guaranteed to destroy the topological phase. We define a mapping that addresses both issues and provides a unified framework for describing disordered topological matter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00784 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Symmetric approximant formalism for statistical topological matter Zijderveld, R. Johanna Chaou, Adam Yanis Day, Isidora Araya Akhmerov, Anton R. Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks The standard approach to characterizing topological matter, computing topological invariants, fails when the symmetry protecting the topological phase is preserved only on average in a disordered system. Because topological invariants rely on enforcing the symmetry exactly, they can overcount phases by incorrectly identifying certain non-robust features as robust. Moreover, in intrinsic statistical topological insulators, enforcing the symmetry exactly is guaranteed to destroy the topological phase. We define a mapping that addresses both issues and provides a unified framework for describing disordered topological matter. |
| title | Symmetric approximant formalism for statistical topological matter |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2601.00784 |