Self-averaging

A self-averaging physical property of a disordered system is one that can be described by averaging over a sufficiently large sample. The concept was introduced by Ilya Mikhailovich Lifshitz.

Frequently in physics one comes across situations where quenched randomness plays an important role. Any physical propertyX of such a system, would require an averaging over all disorder realisations. The system can be completely described by the average [X] where [...] denotes averaging over realisations (“averaging over samples”) provided the relative varianceR_X = V_X / [X]² → 0 as N→∞, where V_X = [X²] − [X]² and N denotes the size of the realisation. In such a scenario a single large system is sufficient to represent the whole ensemble. Such quantities are called self-averaging. Away from criticality, when the larger lattice is built from smaller blocks, then due to the additivity property of an extensive quantity, the central limit theorem guarantees that R_X ~ N⁻¹ thereby ensuring self-averaging. On the other hand, at the critical point, the question whether ${\displaystyle X}$ is self-averaging or not becomes nontrivial, due to long range correlations.

At the pure critical point randomness is classified as relevant if, by the standard definition of relevance, it leads to a change in the critical behaviour (i.e., the critical exponents) of the pure system. It has been shown by recent renormalization group and numerical studies that self-averaging property is lost if randomness or disorder is relevant.^[1] Most importantly as N → ∞, R_X at the critical point approaches a constant. Such systems are called non self-averaging. Thus unlike the self-averaging scenario, numerical simulations cannot lead to an improved picture in larger lattices (large N), even if the critical point is exactly known. In summary, various types of self-averaging can be indexed with the help of the asymptotic size dependence of a quantity like R_X. If R_X falls off to zero with size, it is self-averaging whereas if R_X approaches a constant as N → ∞, the system is non-self-averaging.

Existe una clasificación adicional de los sistemas auto-promediados en fuertes y débiles. Si el comportamiento exhibido es R _X  ~ N ⁻¹ como sugiere el teorema del límite central, mencionado anteriormente, se dice que el sistema es fuertemente auto-promediado. Algunos sistemas muestran una disminución más lenta de la ley de potencias R _X ~ N ^{− z} con 0 < z < 1. Dichos sistemas se clasifican como débilmente auto-promediados. Los exponentes críticos conocidos del sistema determinan el exponente z .       

También debe agregarse que la aleatoriedad relevante no implica necesariamente la ausencia de auto-promediación, especialmente en un escenario de campo medio. ^{[ 2 ]} Los argumentos de RG mencionados anteriormente deben extenderse a situaciones con un límite agudo de la distribución de T _c e interacciones de largo alcance.