Multifractality and statistical localization in the sparse Barrat-Mézard trap model
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908466678857728 |
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| author | Tapias, Diego Sollich, Peter |
| author_facet | Tapias, Diego Sollich, Peter |
| contents | We study within a paradigmatic model for glassy dynamics, the Barrat-Mézard trap model, the effect of a nontrivial network structure in the connectivity among traps. Sparseness of this network has recently been shown to lead to divergences in the bulk of the spectrum of the associated master operator [1, 2]. We analyse here specifically the properties of the relaxation modes that contribute to these spectral divergences. We characterize the statistics of the corresponding wavefunctions and demonstrate that they are localized with multifractal properties. The localization patterns are unrelated to the spatial (network) topology, however, and instead fall within the recently introduced class of statistical localization phenomena [3]. To rationalize these results we develop an effective model that successfully explains both the spectral divergences and the power law tails in the wavefunction entries, and provides a clear physical picture of why the localization is statistical rather than spatial. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multifractality and statistical localization in the sparse Barrat-Mézard trap model Tapias, Diego Sollich, Peter Disordered Systems and Neural Networks We study within a paradigmatic model for glassy dynamics, the Barrat-Mézard trap model, the effect of a nontrivial network structure in the connectivity among traps. Sparseness of this network has recently been shown to lead to divergences in the bulk of the spectrum of the associated master operator [1, 2]. We analyse here specifically the properties of the relaxation modes that contribute to these spectral divergences. We characterize the statistics of the corresponding wavefunctions and demonstrate that they are localized with multifractal properties. The localization patterns are unrelated to the spatial (network) topology, however, and instead fall within the recently introduced class of statistical localization phenomena [3]. To rationalize these results we develop an effective model that successfully explains both the spectral divergences and the power law tails in the wavefunction entries, and provides a clear physical picture of why the localization is statistical rather than spatial. |
| title | Multifractality and statistical localization in the sparse Barrat-Mézard trap model |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2507.19030 |