Adversarial domain adaptation to reduce sample bias of a high energy physics classifier
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Abstract 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 $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification.
1995 ◽
Vol 06
(04)
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pp. 579-584
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A new method combining discrete wavelet transform and neural network for high energy physics problem
2019 ◽
Vol 3
(2)
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pp. 79-81
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1993 ◽
pp. 426-431
Keyword(s):
1993 ◽
Vol 04
(02)
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pp. 95-108
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