Dortmund 2021 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 71: Data analysis, Information technology III

T 71.6: Vortrag

Mittwoch, 17. März 2021, 17:15–17:30, Tu

Performance Studies of the Integration of a Deep-Impact-Parameter-Setsbased Tagger for the ATLAS Experiment b-Tagging Algorithm — •Alexander Froch, Manuel Guth, and Andrea Knue — Albert-Ludwigs Universität Freiburg, Experimentelle Teilchenphysik AG Herten

The identification of the origin of a jet produced in a high-energy collision is an important task and is crucial for most analyses performed at the ATLAS experiment. Different multivariate techniques are used and combined to determine the jet origin. One of these techniques is the Deep Impact Parameter Sets (DIPS) tagger.
The DIPS tagger is a deep neural network based on the Deep Sets architecture. It uses track information of the particles inside the clustered jets for classification. It is part of a new tagging algorithm currently developed in ATLAS. The algorithm itself can distinguish between different jet origins, like light, charm or bottom jets. A good performance was already observed for a training using tt events. For further improvement in the high pT region, jets from Z decays are included in the training.
The performance of this training will be shown along with the impact of the hyper-parameter optimization studies.

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