  Deep learning neural network technique (DNN) is one of the most efficient and
general approach of multivariate data analysis of the collider experiments. The
important step of the analysis is the optimization of the input space for
multivariate technique. In the article we propose the general recipe how to
form the set of low-level observables sensitive for the differences in hard
scattering processes at the colliders. It is shown in the paper that without
any sophisticated analysis of the kinematic properties one can achieve close to
optimal performance of DNN with the proposed general set of low-level
observables.
