Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimising the difference in response of the network to background samples originating from different MC models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the LHC with \(t\bar{t}H\) signal versus \(t\bar{t}b\bar{b}\) background classification and discuss implications and limitations of the method