Separation of a single photon and products of the ${\pi }^0,\eta ,K_s^0$ meson neutral decay channels in the CMS electromagnetic calorimeter using neural network

Abstract

The artificial neural network approach is used for separation of signals from a single photon $\gamma$ and products of the ${\pi }^0,\eta ,K_s^0$ meson neutral decay channels on the basis of the data from the CMS electromagnetic calorimeter alone. Rejection values for the three types of mesons as a function of single photon selection efficiencies are obtained for two Barrel and one Endcap pseudorapidity regions and initial $\Et$ of 20, 40, 60 and 100 GeV.

16 pages, uses cernrep.cls style file

References in corpus (1)

• A comparative study of the D0 neural-network analysis of the top quark non-leptonic decay channel

Cited by in corpus (2)

• Cascade Training Technique for Particle Identification
• A multivariate approach to heavy flavour tagging with cascade training