Bonn 2020 – scientific programme

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T: Fachverband Teilchenphysik

T 58: Higgs: associated production

T 58.1: Talk

Wednesday, April 1, 2020, 16:30–16:45, H-1.002

Optimisation of a deep neural network flavour-tagging algorithm to improve the definition of signal and control regions in the ttH (H → bb) search. — •Thea Engler, Manuel Guth, Andrea Knue, and Gregor Herten — University of Freiburg, Institute of Physics

The search for the ttH (H → bb) signal provides direct access to the top-Higgs Yukawa coupling. This channel has four b-jets in the final state and is suffering from large physics background, which makes b-tagging a crucial tool for this analysis. The dominant and most challenging background process is tt + bb. This process also contains four jets however, wherefore a simple b-tagger will not help to discriminate between signal and background. Nonetheless, the two b-jets from the g → bb splitting can be so close that they are identified as one jet. Identification of these gluon splittings would allow to reject the tt + bb background more efficiently. In this talk, a deep neural network is presented in which the nominal DL1 tagger used by the ATLAS experiment is extended by the g → bb category, and first optimisation studies are shown.