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B-decay data from the Belle experiment at the KEKB collider have asubstantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress thiswe employ deep neural network algorithms. These provide improved signal frombackground discrimination. However, the deep neural network develops asubstantial correlation with the $\Delta E$ kinematic variable used todistinguish signal from background in the final fit due to its relationshipwith input variables. The effect of this correlation is reduced by deploying anadversarial neural network. Overall the adversarial deep neural networkperforms better than a Boosted Decision Tree algorithimn and a commercialpackage, NeuroBayes, which employs a neural net with a single hidden layer.

The use of adversaries for optimal neural network training