2 research outputs found
Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
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 signal versus background
classification.Comment: 15 pages, 8 figures, to be submitted to JINS