Münster 2017 – scientific programme

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T: Fachverband Teilchenphysik

T 73: Higgs-Boson 6 (Zerfälle in Tau-Leptonen)

T 73.8: Talk

Wednesday, March 29, 2017, 18:30–18:45, JUR 3

Machine learning for H→ττ — •Carina Brandt, Adrian Perieanu, Oliver Rieger, Peter Schleper, Daniel Troendle, and Annika Vanhoefer — University of Hamburg

The search for H→ ττ at the LHC is a challenging task, due to the overwhelming background from the Z→ττ process. Today, a dedicated mass reconstruction which is based on a likelihood method is used very successfully to reconstruct the mass of the higgs. With the significant advances in machine learning techniques in the last years, new and improved methods are now applicable to high energy physics data analysis. In this study modern machine learning (ML) algorithms have been exploited to improve the reconstruction of the mass and hence improve the seperation of Higgs and Z Bosons processes at CMS. First results based on Monte Carlo simulation at a centre of mass energy √s=13 TeV will be presented.