Münster 2017 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

T: Fachverband Teilchenphysik

T 3: Higgs-Boson 2 (assoziierte Produktion)

T 3.5: Talk

Monday, March 27, 2017, 17:45–18:00, JUR 5

Event Categorization Using Deep Neural Networks for ttH(H→bb) at the CMS Experiment — •Yannik Rath, Florian von Cube, Martin Erdmann, Benjamin Fischer, Robert Fischer, Erik Geiser, Thorben Quast, and Marcel Rieger — III. Physikalisches Institut A, RWTH Aachen University

The analysis of top-quark pair associated Higgs production enables a direct measurement of the top-Higgs Yukawa coupling. In ttH(H→bb) analyses, multiple event categories are commonly used in order to simultaneously constrain signal and background processes. A typical approach is to categorize events according to both their jet and b-tag multiplicities.

The performance of this procedure is limited by the b-tagging efficiency and decreases for events with high b-tag multiplicity such as in ttH(H→bb).

Machine learning algorithms provide an alternative method of event categorization. A promising choice for this kind of multiclass classification problem are deep neural networks (DNNs). In this talk, we present a categorization scheme using DNNs that is based on the underlying physics processes of events in the semileptonic ttH(H→bb) decay channel. Furthermore, we discuss different methods employed for improving the network’s categorization performance.