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000905636 1001_ $$00000-0003-3210-1722$$aClavijo, J. M.$$b0$$eCorresponding author
000905636 245__ $$aAdversarial domain adaptation to reduce sample bias of a high energy physics event classifier
000905636 260__ $$aBristol$$bIOP Publishing$$c2022
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000905636 520__ $$aWe apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification and discuss implications and limitations of the method.
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000905636 536__ $$0G:(DE-HGF)2020_ZT-I-PF-5-3$$aZT-I-PF-5-3 - Deep Generative models for fast and precise physics Simulation (DeGeSim) (2020_ZT-I-PF-5-3)$$c2020_ZT-I-PF-5-3$$x1
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000905636 7001_ $$0P:(DE-HGF)0$$aGlaysher, P.$$b1
000905636 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b2$$ufzj
000905636 7001_ $$00000-0003-3121-395X$$aKatzy, J. M.$$b3$$eCorresponding author
000905636 773__ $$0PERI:(DE-600)3017004-7$$a10.1088/2632-2153/ac3dde$$gVol. 3, no. 1, p. 015014 -$$n1$$p015014$$tMachine learning: science and technology$$v3$$x2632-2153$$y2022
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