Speaker
Description
The analysis of top-quark pair associated Higgs boson 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 contributions during a fit to data. 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 diminishes 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 multi-class classification applications 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 semi-leptonic ttH (H→bb) decay channel. Furthermore, we discuss different methods employed for improving the network’s categorization performance.
| Intended contribution length | 20 minutes |
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