Machine learning for event classification and automated discovery of new physics at the LHC experiments

A method for an automated preparation of the feature space for various supervised artificial neural networks (ANN) used for searches for new physics at the LHC is presented. The proposed standardization of the ANN inputs allows "fingerprinting" of final state of collision events, translating experimental data from particle colliders to the language convenient for machine learning techniques. This can simplify searches for experimental signatures of new physics at the LHC. The method was illustrated using Monte Carlo event generators for several models beyond the Standard Model. The Monte Carlo simulations were also used to illustrate the usability of this approach for general event classification problems.