General recipe to form input space for deep learning analysis of HEP scattering processes

Deep learning neural network (DNN) technique 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 paper we propose the general recipe how to form the set of low-level observables sensitive to 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.