Optimization of the input space for deep learning data analysis in HEP.

Abstract

Deep learning neural network technique is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of such analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to find the general set of low-level observables sensitive for the differences in the collider hard processes.