  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

Machine Learning;Machine Learning;High Energy Physics - Experiment;High Energy Physics - Phenomenology