Dortmund 2021 – scientific programme

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T: Fachverband Teilchenphysik

T 71: Data analysis, Information technology III

T 71.9: Talk

Wednesday, March 17, 2021, 18:00–18:15, Tu

Improvement of the jet-parton assignment in ttH(bb) events using machine-learning techniques — •Daniel Bahner, Andrea Knue, and Gregor Herten — Albert-Ludwigs-University, Freiburg, Germany

The associated production of a Higgs boson and a top quark pair allows to directly measure the Higgs-top Yukawa coupling, which can be sensitive to Beyond Standard Model physics. In the studies presented, the process of interest is the semileptonic decay of the tt pair accompanied by a bb pair resulting from the most prominent Higgs decay. In this topology, four b-jets and two light jets are expected. This Higgs decay channel suffers from irreducible background due to tt+bb production. Furthermore, the full reconstruction of this final state proves difficult because of the ambiguities in assigning the jets to their original parton.

In the latest publication, a Boosted Decision Tree was used for the jet-parton assignment. In the studies presented, a Deep Neural Network is used that has been previously trained in the scope of a master thesis. Optimization studies of the network architecture and the impact of using the new ATLAS b-tagging algorithm DL1r will be shown.