Critical exponent
Critical exponents describe the behavior of physical quantities near continuous phase transitions. It is believed, though not proven, that they are universal, i.e. they do not depend on the details of the physical system, but only on some of its general features. For instance, for ferromagnetic systems, the critical exponents depend only on:
 the dimension of the system
 the range of the interaction
 the spin dimension
These properties of critical exponents are supported by experimental data. Analytical results can be theoretically achieved in mean field theory in high dimensions or when exact solutions are known such as the twodimensional Ising model. The theoretical treatment in generic dimensions requires the renormalization group approach. Phase transitions and critical exponents appear in many physical systems such as water at the liquidvapor transition, in ferro or antiferromagnetic systems, in superconductivity, in percolation, in systems of particles that diffuse and undergo chemical reactions, in turbulent fluids,.... The critical dimension above which mean field exponents are valid varies with the systems and can even be infinite. It is 4 for the liquidvapor transition, 6 for percolation and probably infinite for turbulence.^{[1]} Mean field critical exponents are also valid for random graphs, such as Erdős–Rényi graphs, which can be regarded as infinite dimensional systems.^{[2]}
Contents
 1 Definition
 2 The most important critical exponents
 3 Mean field critical exponents of Isinglike systems
 4 Experimental values
 5 Scaling functions
 6 Scaling relations
 7 Anisotropy
 8 Multicritical points
 9 Static versus dynamic properties
 10 Transport properties
 11 Selforganized criticality
 12 Percolation Theory
 13 See also
 14 External links and literature
 15 References
Definition
The control parameter that drives the phase transitions phase transition is often the temperature but it can also be a pressure, a magnetic field... For the sake of simplicity, let us assume that it is the temperature (the translation to another control prameter is straightforward). The temperature at which the transition occurs is called the critical temperature T_{c}. We want to describe the behavior of a physical quantity f in terms of a power law around the critical temperature; we introduce the reduced temperature
which is zero at the phase transition, and define the critical exponent :
This results in the power law we were looking for:
It is important to remember that this represents the asymptotic behavior of the function f(τ) as τ → 0.
More generally one might expect
The most important critical exponents
Let us assume that the system has two different phases characterized by an order parameter Ψ, which vanishes at and above T_{c}.
Consider the disordered phase (τ > 0), ordered phase (τ < 0) and critical temperature (τ = 0) phases separately. Following the standard convention, the critical exponents related to the ordered phase are primed. It is also another standard convention to use superscript/subscript + (−) for the disordered (ordered) state. In general spontaneous symmetry breaking occurs in the ordered phase.
Ψ  order parameter (e.g. ρ − ρ_{c}/ρ_{c} for the liquid–gas critical point, magnetization for the Curie point, etc.) 
τ  T − T_{c}/T_{c} 
f  specific free energy 
C  specific heat; −T∂^{2}f/∂T^{2} 
J  source field (e.g. P − P_{c}/P_{c} where P is the pressure and P_{c} the critical pressure for the liquidgas critical point, reduced chemical potential, the magnetic field H for the Curie point) 
χ  the susceptibility, compressibility, etc.; ∂ψ/∂J 
ξ  correlation length 
d  the number of spatial dimensions 
⟨ψ(x→) ψ(y→)⟩  the correlation function 
r  spatial distance 
The following entries are evaluated at J = 0 (except for the δ entry)



The critical exponents can be derived from the specific free energy f(J,T) as a function of the source and temperature. The correlation length can be derived from the functional FJ;T.
These relations are accurate close to the critical point in two and threedimensional systems. In four dimensions, however, the power laws are modified by logarithmic factors. These do not appear in dimensions arbitrarily close to but not exactly four, which can be used as a way around this problem.^{[3]}
Mean field critical exponents of Isinglike systems
The classical Landau theory (also known as mean field theory) values of the critical exponents for a scalar field (of which the Ising model is the prototypical example) are given by
If we add derivative terms turning it into a mean field Ginzburg–Landau theory, we get
One of the major discoveries in the study of critical phenomena is that mean field theory of critical points is only correct when the space dimension of the system is higher than a certain dimension called the upper critical dimension which excludes the physical dimensions 1, 2 or 3 in most cases. The problem with mean field theory is that the critical exponents do not depend on the space dimension. This leads to a quantitative discrepancy below the critical dimensions, where the true critical exponents differ from the mean field values. It can even lead to a qualitative discrepancy at low space dimension, where a critical point in fact can no longer exist, even though mean field theory still predicts there is one. This is the case for the Ising model in dimension 1 where there is no phase transition. The space dimension where mean field theory becomes qualitatively incorrect is called the lower critical dimension.
Experimental values
The most accurately measured value of α is −0.0127(3) for the phase transition of superfluid helium (the socalled lambda transition). The value was measured on a space shuttle to minimize pressure differences in the sample.^{[4]} This value is in a significant disagreement with the most precise theoretical determination by a combination of Monte Carlo and high temperature expansion techniques. Other techniques give results in agreement in the experiment but are less precise.^{[5]}
Scaling functions
In light of the critical scalings, we can reexpress all thermodynamic quantities in terms of dimensionless quantities. Close enough to the critical point, everything can be reexpressed in terms of certain ratios of the powers of the reduced quantities. These are the scaling functions.
The origin of scaling functions can be seen from the renormalization group. The critical point is an infrared fixed point. In a sufficiently small neighborhood of the critical point, we may linearize the action of the renormalization group. This basically means that rescaling the system by a factor of a will be equivalent to rescaling operators and source fields by a factor of a^{Δ} for some Δ. So, we may reparameterize all quantities in terms of rescaled scale independent quantities.
Scaling relations
It was believed for a long time that the critical exponents were the same above and below the critical temperature, e.g. α ≡ α′ or γ ≡ γ′. It has now been shown that this is not necessarily true: When a continuous symmetry is explicitly broken down to a discrete symmetry by irrelevant (in the renormalization group sense) anisotropies, then the exponents γ and γ′ are not identical.^{[6]}
Critical exponents are denoted by Greek letters. They fall into universality classes and obey the scaling relations
These equations imply that there are only two independent exponents, e.g., ν and η. All this follows from the theory of the renormalization group.
Anisotropy
There are some anisotropic systems where the correlation length is direction dependent. For percolation see Dayan et al.^{[7]}
Directed percolation can be also regarded as anisotropic percolation. In this case the critical exponents are different and the upper critical dimension is 5.^{[8]}
Multicritical points
More complex behavior may occur at multicritical points, at the border or on intersections of critical manifolds. For a simple model with multicritical points see reference.^{[9]}
Static versus dynamic properties
The above examples exclusively refer to the static properties of a critical system. However dynamic properties of the system may become critical, too. Especially, the characteristic time, τ_{char}, of a system diverges as τ_{char} ∝ ξ^{z}, with a dynamical exponent z. Moreover, the large static universality classes of equivalent models with identical static critical exponents decompose into smaller dynamical universality classes, if one demands that also the dynamical exponents are identical. For critical exponents for dynamics in percolation systems see reference.^{[1]}
The critical exponents can be computed from conformal field theory.
See also anomalous scaling dimension.
Transport properties
Critical exponents also exist for transport quantities like viscosity and heat conductivity. A recent study suggests that critical exponents of percolation play an important role in city traffic.^{[10]}
Selforganized criticality
Critical exponents also exist for self organized criticality for dissipative systems.
Percolation Theory
Phase transitions and critical exponents appear also in percolation processes where the concentration of occupied sites or links play the role of temperature. See percolation critical exponents. For percolation the critical exponents are different from Ising. For example, in the mean field for percolation^{[1]} compared to for Ising.
See also
 Complex networks
 Random graphs
 Rushbrooke inequality
 Widom scaling
 Ising critical exponents
 Percolation critical exponents
 Network science
 Percolation theory
 Graph theory
External links and literature
 Hagen Kleinert and Verena SchulteFrohlinde, Critical Properties of φ^{4}Theories, World Scientific (Singapore, 2001); Paperback ISBN 9810246587
 Toda, M., Kubo, R., N. Saito, Statistical Physics I, SpringerVerlag (Berlin, 1983); Hardcover ISBN 3540114602
 J.M.Yeomans, Statistical Mechanics of Phase Transitions, Oxford Clarendon Press
 H. E. Stanley Introduction to Phase Transitions and Critical Phenomena, Oxford University Press, 1971
 A. Bunde and S. Havlin (editors), Fractals in Science, Springer, 1995
 A. Bunde and S. Havlin (editors), Fractals and Disordered Systems, Springer, 1996
 Universality classes from Sklogwiki
 ZinnJustin, Jean (2002). Quantum field theory and critical phenomena, Oxford, Clarendon Press (2002), ISBN 0198509235
 ZinnJustin, J. (2010). "Critical phenomena: field theoretical approach" Scholarpedia article Scholarpedia, 5(5):8346.
 F. Leonard and B. Delamotte Critical exponents can be different on the two sides of a transition: A generic mechanism https://arxiv.org/abs/1508.07852
References
 ^ ^{a} ^{b} ^{c} Bunde, Armin; Havlin, Shlomo (1996). "Percolation I". Fractals and Disordered Systems. Springer, Berlin, Heidelberg. pp. 59–114. doi:10.1007/9783642848681_2. ISBN 9783642848704.
 ^ Cohen, Reuven; Havlin, Shlomo (2010). "Introduction". Complex Networks: Structure, Robustness and Function. Cambridge University Press. pp. 1–6. doi:10.1017/cbo9780511780356.001. ISBN 9780521841566.
 ^ 't Hooft, G.; Veltman, M. (1972). "Regularization and Renormalization of Gauge Fields" (PDF). Nucl. Phys. B. 44: 189–213. doi:10.1016/05503213(72)902799.
 ^ Lipa, J. A.; Nissen, J.; Stricker, D.; Swanson, D.; Chui, T. (2003). "Specific heat of liquid helium in zero gravity very near the lambda point". Physical Review B. 68 (17): 174518. arXiv:condmat/0310163. Bibcode:2003PhRvB..68q4518L. doi:10.1103/PhysRevB.68.174518.
 ^ Vicari, Ettore (2007). Critical phenomena and renormalizationgroup ﬂow of multiparameter Φ^{4} ﬁeld theories. The XXV International Symposium on Lattice Field Theory, July 30  August 4, 2007, Regensburg, Germany. p. 7 (Table 2). arXiv:0709.1014v2.
 ^ Leonard, F.; Delamotte, B. (2015). "Critical exponents can be different on the two sides of a transition". Phys. Rev. Lett. 115 (20): 200601. arXiv:1508.07852. Bibcode:2015PhRvL.115t0601L. doi:10.1103/PhysRevLett.115.200601. PMID 26613426.
 ^ Dayan, I.; Gouyet, J.F.; Havlin, S. (1991). "Percolation in multilayered structures". J. Phys. A. 24 (6): L287. Bibcode:1991JPhA...24L.287D. doi:10.1088/03054470/24/6/007.
 ^ Kinzel, W. (1982). Deutscher, G. (ed.). "Directed Percolation". Percolation and Processes.
 ^ Majdandzic, A.; Podobnik, B.; Buldyrev, S.V.; Kenett, D.Y.; Havlin, S.; Stanley, H.E. (2014). "Spontaneous recovery in dynamical networks". Nature Physics. 10 (1): 34. Bibcode:2014NatPh..10...34M. doi:10.1038/nphys2819.
 ^ Zeng, Guanwen; Li, Daqing; Gao, Liang; Gao, Ziyou; Havlin, Shlomo (20170910). "Switch of critical percolation modes in dynamical city traffic". arXiv:1709.03134. Bibcode:2017arXiv170903134Z. Cite journal requires
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