Use of Neural Nets to Measure The? Polarization and its Bayesian Interpretation

We have tested a neural network (NN) technique as a method to determine the helicity of the τ particles in the process: e+e−→(Z0, γ*)→τ+τ−→(ρν)(ρν). It takes into account in a natural way the fact that both taus have different helicity and gives efficiencies comparable to the Bayesian method. We have found this “academic” example a nice way to introduce the analytical interpretation of the net output, showing that these neural nets techniques are equivalent to a Bayesian Decision Rule.