We investigate analytically the magnetization of Heisenberg ferromagnetic lattices in one and two dimensions, and we derive approximate expressions that are valid at high and low temperatures. In the case of the spin-
21 Heisenberg XX chain in a transverse field, we show that, when the applied magnetic field
h exceeds its critical value
hc=J, where
J is the exchange coupling constant, the magnetization per site deviates at low temperatures from its saturation value,
21, following a power series with terms involving the power laws
T1/2,
T3/2,
T5/2 ..etc. When
h<hc, the zero temperature magnetization per spin turns out to be equal to
π1arcsin(hch). In this case, the temperature dependence of the magnetization is given by a series expansion with power laws of the form
T,
T3,
T5,...etc. In both cases, the coefficients of the expansions are temperature-dependent and are explicitly derived. Using he properties of the Eulerian polynomials, we show that, because of the fast convergence of the derived series for the Fermi-Dirac and the Bose-Einstein distributions, it is possible (in particular in strong magnetic fields) to express the magnetization of the Heisenberg model in a simple analytical form. Furthermore, the analytical results are compared with the exact numerical ones.
We derive upper and lower bounds on the fidelity susceptibility in terms of macroscopic thermodynamical quantities, such as the susceptibilities and thermal average values. The quality of the bounds is checked against the exact expressions for a single spin in an external magnetic field. Their usefulness is illustrated by two examples of many-particle models which are exactly solved in the thermodynamic limit: the Dicke superradiance model and the single-impurity Kondo model. It is shown that, when divergent behavior is considered, the fidelity susceptibility and the thermodynamic susceptibility are equivalent for a large class of models exhibiting critical behavior.
Recent results on the upper-bound property of the magnetization in certain approximations to ferromagnetic spin systems are here used to derive a rigorous upper bound for the critical temperature of the one-dimensional ferromagnetic
n21 Ising spin system. We find that ${\ensuremath{\beta}}_{c}J\ensuremath{\ge}0.441({\ensuremath{\beta}}_{c}=\frac{1}{k{T}_{c}})$. The best previous bound was 0.360. A further improvement by the same technique is possible.
Bounds are presented relating zero-field static isothermal magnetic susceptibilities to the mean-square fluctuations of corresponding magnetization variables. The lower bounds contain the first frequency moment of a spectral density. When this moment $\overline{\ensuremath{\omega}}$ approaches zero, the upper and lower bounds merge, and the susceptibility is determined by the mean-square fluctuation. In particular, if the susceptibility diverges at a temperature
Tc, and if the expectation of the double commutator appearing in $\overline{\ensuremath{\omega}}$ is finite at and near
Tc, then the fluctuation and the susceptibility diverge in the same manner, and their critical exponents will be identical.
An estimator for the dynamical temperature in an arbitrary ensemble is derived in the framework of the conjugate variables theorem. We prove directly that its average indeed gives the inverse temperature and that it is independent of the ensemble. We test this estimator numerically by a simulation of the two-dimensional XY model in the canonical ensemble. As this model is critical in the whole region of temperatures below the Berezinski-Kosterlitz-Thouless critical temperature T_{BKT}, we use a generalization of Wolff's unicluster algorithm. The numerical results allow us to confirm the robustness of the analytical expression for the microscopic estimator of the temperature. This microscopic estimator has also the advantage that it gives a direct measure of the thermalization process and can be used to compute absolute errors associated with statistical fluctuations. In consequence, this estimator allows for a direct, absolute, and stringent test of the ergodicity of the underlying Markov process, which encodes the algorithm used in a numerical simulation.