Critical properties of disordered XY model on sparse random graphs
arXiv:1706.08899
Abstract
This thesis focuses on the XY model, the simplest vector spin model, used for describing numerous physical systems. It is studied for different sources of quenched disorder: random couplings, random fields, or both them. The belief propagation algorithm and the cavity method are exploited to solve the model on the sparse topology provided by Bethe lattices. It is found that the discretized version of the XY model, the so-called -state clock model, provides a reliable and efficient proxy for the continuous model with an error going to zero exponentially in , so implying a remarkable speedup in numerical simulations. Interesting results regard the low-temperature solution of the spin glass XY model, which is by far more unstable toward the replica symmetry broken phase with respect to what happens in discrete models. Moreover, even the random field XY model with ferromagnetic couplings exhibits a replica symmetry broken phase, at variance with both the fully connected version of the same model and the diluted random field Ising model, as a further evidence of a more pronounced glassiness of the diluted XY model. Then, the instabilities of the spin glass XY model in an external field are characterized, recognizing different critical lines according to the different symmetries of the external field. Finally, the inherent structures in the energy landscape of the spin glass XY model in a random field are described, exploiting the capability of the zero-temperature belief propagation algorithm to actually reach the ground state of the system. Remarkably, the density of soft modes in the Hessian matrix shows a non-mean-field behaviour, typical of glasses in finite dimension, while the critical point of replica symmetry instability predicted by the belief propagation algorithm seems to correspond to a delocalization of such soft modes.
arXiv commentPhD thesis in Physics, defended on February 16th, 2017 at Sapienza University of Rome. Advisor: prof. Federico Ricci-Tersenghi
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