Hamburg 2016 – scientific programme

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T: Fachverband Teilchenphysik

T 67: Higgs-Boson (assoziierte Produktion) III

T 67.2: Talk

Wednesday, March 2, 2016, 17:00–17:15, VMP5 HS B1

Reconstruction of ttH (H→ bb) Events using Deep Neural Networks with the CMS Detector — •Marcel Rieger, Martin Erdmann, Benjamin Fischer, Robert Fischer, Fabian Heidemann, Thorben Quast, and Yannik Rath — III. Physikalisches Institut A, RWTH Aachen University

The measurement of Higgs boson production in association with top-quark pairs (ttH) is an important goal of Run 2 of the LHC as it allows for a direct measurement of the underlying Yukawa coupling. Due to the complex final state, however, the analysis of semi-leptonic ttH events with the Higgs boson decaying into a pair of bottom-quarks is challenging.

A promising method for tackling jet parton associations are Deep Neural Networks (DNN). While being a widely spread machine learning algorithm in modern industry, DNNs are on the way to becoming established in high energy physics.

We present a study on the reconstruction of the final state using DNNs, comparing to Boosted Decision Trees (BDT) as benchmark scenario. This is accomplished by generating permutations of simulated events and comparing them with truth information to extract reconstruction efficiencies.