Towards a fully automated computation of RG-functions for the 3-$d$ O(N) vector model: Parametrizing amplitudes

Details Towards a fully automated computation of RG-functions for the 3-$d$ O(N) vector model: Parametrizing amplitudes Towards a fully automated computation of RG-functions for the 3-$d$ O(N) vector model: Parametrizing amplitudes

2005-12-10

arxiv.org/abs/cond-mat/0512222v1

J.Stat.Mech.0602:P02007,2006

Physics

Condensed Matter

Statistical Mechanics

54 pages, 17 figures and 4 tables

Scientific paper

10.1088/1742-5468/2006/02/P02007

Within the framework of field-theoretical description of second-order phase transitions via the 3-dimensional O(N) vector model, accurate predictions for critical exponents can be obtained from (resummation of) the perturbative series of Renormalization-Group functions, which are in turn derived --following Parisi's approach-- from the expansions of appropriate field correlators evaluated at zero external momenta. Such a technique was fully exploited 30 years ago in two seminal works of Baker, Nickel, Green and Meiron, which lead to the knowledge of the $\beta$-function up to the 6-loop level; they succeeded in obtaining a precise numerical evaluation of all needed Feynman amplitudes in momentum space by lowering the dimensionalities of each integration with a cleverly arranged set of computational simplifications. In fact, extending this computation is not straightforward, due both to the factorial proliferation of relevant diagrams and the increasing dimensionality of their associated integrals; in any case, this task can be reasonably carried on only in the framework of an automated environment. On the road towards the creation of such an environment, we here show how a strategy closely inspired by that of Nickel and coworkers can be stated in algorithmic form, and successfully implemented on the computer. As an application, we plot the minimized distributions of residual integrations for the sets of diagrams needed to obtain RG-functions to the full 7-loop level; they represent a good evaluation of the computational effort which will be required to improve the currently available estimates of critical exponents.

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