Application of neural networks optimized by genetic algorithms to Higgs boson search

This paper describe an application of a neural network approach to SM (standard model) and MSSM (minimal supersymetry standard model) Higgs search in the associated production (tt) over barH with H --> b (b) over bar. This decay channel is considered as a discovery channel for Higgs scenarios for Higgs boson masses in the range 80 - 130 GeV. Neural network model with a special type of data flow is used to separate t (t) over bar jj background from H --> b (b) over bar events. Used neural network combine together a classical neural network approach and linear decision tree separation process. Parameters of these neural networks are randomly generated and population of predefined size of those networks is learned to get initial generation for the following genetic algorithm optimization process. A genetic algorithm principles are used to tune parameters of further neural network individuals derived from previous neural networks by GA operations of crossover and mutation. The goal of this GA process is optimization of the final neural network performance. Our results show that NN approach is applicable to the problem of Higgs boson detection. Neural network filters can be used to emphasize difference Of M-bb distribution for events accepted by filter (with better signal/background rate) and M-bb distribution for original events (with original signal/background rate) under condition that there is no loss of significance. This improvement of the shape Of Mbb distribution can be used as a criterion of existence of Higgs boson decay in considered discovery channel.