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Self-averaging is a property of a system where a physical property of a complete disordered system can be described by averaging over a sufficiently large sample;
it was introduced by Ilya Mikhailovich Lifshitz.
## Definition

## Non self-averaging systems

## Strong and weak self-averaging

## References

Very often in physics one comes across situations where quenched randomness plays an important role. Therefore, any physical property $X$ of such a disordered system, would require an averaging over all realisations. It would suffice to have a description in terms of the average $[X]$ where $[...]$ denotes an averaging over realisations (“averaging over samples”) provided the relative variance R_{X} = V_{X}/[X]^{2} → 0 for large $X$, where V_{X} = [X^{2}] − [X]^{2}. In such a case a single large system is enough to represent the whole ensemble. Such quantities are called self-averaging. Off criticality, when one builds up a large lattice from smaller blocks, then due to the additivity property of an extensive quantity, central limit theorem guarantees that R_{X} → N^{−1} ensuring self-averaging. In contrast, at a critical point, due to long range correlations the answer whether $X$ is self-averaging or not becomes nontrivial.

Randomness at a pure critical point is classified as relevant or irrelevant 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. Recent renormalization group and numerical studies have shown that if randomness or disorder is relevant, then self-averaging property is lost . In particular, R_{X} at the critical point approaches a constant as N → ∞. Such systems are called non self-averaging. A serious consequence of this is that unlike the self-averaging case, even if the critical point is known exactly, statistics in numerical simulations cannot be improved by going over to larger lattices (large N). Let us recollect the definitions of various types of self-averaging with the help of the asymptotic size dependence of a quantity like R_{X}. If R_{X} approaches a constant as N → ∞, the system is non-self-averaging while if R_{X} decays to zero with size, it is self-averaging.

Self-averaging systems are further classified as strong and weak. If the decay is R_{X} ~ N^{−1} as suggested by the central limit theorem, mentioned earlier, the system is said to be strongly self-averaging. There is yet another class of systems which shows a slower power law decay R_{X} ~ N^{−z} with 0 < z < 1. Such cases are known as weakly self-averaging. The exponent z is determined by the known critical exponents of the system.

It must also be added that relevant randomness does not necessarily imply non self-averaging, especially in a mean-field scenario
. An extension of
the RG arguments mentioned above to encompass situations with sharp limit of T_{c} distribution and long range interactions, may shed light on this.

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Last updated on Friday August 22, 2008 at 15:48:50 PDT (GMT -0700)

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This article is licensed under the GNU Free Documentation License.

Last updated on Friday August 22, 2008 at 15:48:50 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

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