Saddle index properties, singular topology, and its relation to thermodynamical singularities for a phi4 mean field model
D. A. Garanin, R. Schilling, A. Scala
Abstract
We investigate the potential energy surface of a phi4 model with infinite range interactions. All stationary points can be uniquely characterized by three real numbers $α+, alpha0, alpha- with alpha+ + alpha0 + alpha- = 1, provided that the interaction strength mu is smaller than a critical value. The saddle index ns is equal to alpha0 and its distribution function has a maximum at nsmax = 1/3. The density p(e) of stationary points with energy per particle e, as well as the Euler characteristic chi(e), are singular at a critical energy ec(mu), if the external field H is zero. However, ec(mu) ≠ upsilonc(mu), where upsilonc(mu) is the mean potential energy per particle at the thermodynamic phase transition point Tc. This proves that previous claims that the topological and thermodynamic transition points coincide is not valid, in general. Both types of singularities disappear for H ≠ 0. The average saddle index barns as function of e decreases monotonically with e and vanishes at the ground state energy, only. In contrast, the saddle index ns as function of the average energy bare(ns) is given by ns(bare) = 1+4bare (for H=0) that vanishes at bare = -1/4 > upsilon0, the ground state energy.
Create a lesson
Related papers
Dynamically Reentrant Skyrmion Phase in Oscillating Magnetic Fields
Z. Demir Vatansever, E. Vatansever, A. Vasilopoulos et al.
Mutual linearity in semi-Markov networks
Julian B. Voits, Ulrich S. Schwarz
Steady states in asymmetric exclusion processes with reactions: Fluctuations and non-mean field behaviour
Rohn Chatterjee, Atri Goswami, Sudip Mukherjee
Marginal Operator Deformations and Pseudocriticality in Baxter--Wu Models
Hyun-Yong Lee, Naoki Kawashima, Nikolaos G. Fytas et al.
Kardar--Parisi--Zhang density hydrodynamics in a coherent XXZ--ASEP Lindbladian
Yansheng Liu, Daoguang Li, Kaixiang Lu et al.
Brownian gyrator model with stochastic resetting
Satya N. Majumdar, Gregory Schehr, Pascal Viot et al.