Speaker
Tobias Lösche
(University of Hamburg)
Description
A precise determination of the interactions of the Higgs boson with other SM particles is a crucial part of the LHC physics program. When determining the top Yukawa coupling in ttH(bb) events, deep learning plays an integral role. In the single-lepton channel, multivariate approaches using deep neural networks (DNNs) achieve state-of-the-art performance in signal/background classification.
A particular challenge of this analysis is the discrimination of ttH(bb) events from the irreducible tt + bb background. Considering the combinatorial assignment of jets offers a possible means to deal with this problem and thus further improve performance. To achieve this, an attention-based DNN classifier (COBRA) was developed, whose results are presented in this talk.
Primary authors
Gregor Kasieczka
(Institut fuer Experimentalphysik / UHH)
Tobias Lösche
(University of Hamburg)
