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Aug 28 – 31, 2018
EPFL
Europe/Zurich timezone

【365】 Deep Neural Networks for signal/background classification in associated Higgs production with a vector boson decaying into a b-bbar quark

Aug 31, 2018, 12:15 PM
15m
CE 3 (EPFL)

CE 3

EPFL

Talk Nuclear, Particle- and Astrophysics (TASK) Nuclear, Particle- & Astrophysics (TASK)

Speaker

Pirmin Berger (ETH Zurich (CH))

Description

In 2017 the first evidence in CMS data of the Higgs boson (originally discovered in 2012 by ATLAS and CMS collaborations) decaying into a pair of b-bbar quarks has been observed with a significance of 3.3 sigma for associated Higgs production with a vector boson. This analysis is dominated by a large background of V+Jets events and therefore multivariate methods are used to classify signal and background. A better discrimination of signal and background will increase the significance and can contribute to raise it over the threshold needed for an observation. Therefore, new methods, e.g. deep neural networks are explored and their performance in this analysis will be presented together with a general overview of the analysis strategy and results.

Primary author

Pirmin Berger (ETH Zurich (CH))

Presentation materials