A new method to evaluate asymptotic numerical models by data mining techniques

This paper is devoted to a new approach based on data mining to evaluate the efficiency of numerical asymptotic models. We first propose an asymptotic paraxial approximations to model ultrarelativistic particles. Then, we use data mining methods that directly deal with numerical results of simulations, to understand what each order of the asymptotic expansion brings to the simulation results. This new approach offers the possibility to understand, on the numerical results themselves, the efficiency of an asymptotic model, or to compare different asymptotic models, one to each other.