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Deep learning neural network technique (DNN) is one of the most efficient andgeneral approach of multivariate data analysis of the collider experiments. Theimportant step of the analysis is the optimization of the input space formultivariate technique. In the article we propose the general recipe how toform the set of low-level observables sensitive for the differences in hardscattering processes at the colliders. It is shown in the paper that withoutany sophisticated analysis of the kinematic properties one can achieve close tooptimal performance of DNN with the proposed general set of low-levelobservables.

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