Neural network classification of quark and gluon jets

We demonstrate that there are characteristics common to quark jets and to gluon jets regardless of the interaction that produced them. The classification technique we use depends on the mass of the jet as well as the center-of-mass energy of the hard subprocess that produces the jet. In addition, we present the quark-gluon separability results of an artificial neural network trained on three-jet ${\mathit{e}}^{+}$${\mathit{e}}^{\mathrm{\ensuremath{-}}}$ events at the ${\mathit{Z}}^{0}$ mass, using a back-propagation algorithm. The inputs to the network are the longitudinal momenta of the leading hadrons in the jet. We tested the network with quark and gluon jets from both ${\mathit{e}}^{+}$${\mathit{e}}^{\mathrm{\ensuremath{-}}}$\ensuremath{\rightarrow}3 jets and p\ifmmode\bar\else\textasciimacron\fi{}p\ensuremath{\rightarrow}2 jets. Finally, we compare the performance of the artificial neural network with the results of making well chosen physical cuts.