Neural networks for Higgs search

Autor: F. Block, G. Brugnola, L. Cifarelli, Despina Hatzifotiadou, F. Anselmo, G. La Commare, M. Marino
Rok vydání: 1994
Předmět:
Zdroj: Il Nuovo Cimento A. 107:129-141
ISSN: 1826-9869
0369-3546
DOI: 10.1007/bf02813077
Popis: We describe an approach to the heavy-Higgs (mH=750 GeV) search by means of a neural network (NN) in pp collisions at \( \sqrt s = 16 \), TeV (LHC), 40 TeV (SSC) and 200 TeV (ELN/Eloisatron). The mechanisms we considered for Higgs production are gluon fusion and vector boson fusion, letting the H0 decay through the channel H0→Z0Z0→μ+μ-μ+μ-. The overall background to the Higgs signal was assumed to consist of the QCD continuum production of Z0 pairs, where each Z0 was forced to decay into muons. Using Monte Carlo simulated events at each energy, we trained a neural network to distinguish signal from background and evaluated its performances as an event classifier. The results are promising and indicate that neural networks could be efficiently used for event selection in future experiments at super-high energy.
Databáze: OpenAIRE
Externí odkaz: