Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
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We apply adversarial domain adaptation to reduce sample bias in a classification machine learning algorithm. We add a gradient reversal layer to a neural network to simultaneously classify signal versus background events, while minimising the difference of the classifier response to a background sample using an alternative MC model. We show this on the example of simulated events at the LHC with tt̅H signal versus tt̅bb̅ background classification.
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