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Karlsruhe 2024 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 103: Top physics 4 (tt+X)

T 103.3: Vortrag

Donnerstag, 7. März 2024, 16:30–16:45, Geb. 30.95: Audimax

tt+heavy flavor event classification with graph neural networks at the CMS experiment — •Emanuel Pfeffer1, Ulrich Husemann1, Rufa Rafeek1, Jan van der Linden2, and Michael Waßmer11Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT) — 2Institute of Experimental Particle Physics and Gravity, Ghent University (BE)

Processes in which a top quark-antiquark pair is produced in association with additional heavy flavor jets are difficult to separate from each other. Such processes include tt+bb and tt+cc, where the additional heavy flavor quark-antiquark pair stems from gluon splitting, as well as tt+H with H→bb and tt+Z with Z→bb. Machine learning methods based on graph neural networks are promising techniques for enhancing the classification accuracy of these events in the tt+heavy flavor phase space. In this talk the latest status of a simultaneous measurement of the production cross section of a top quark-antiquark pair in association with heavy flavor jets in the dileptonic decay channel at the CMS experiment is presented.

Keywords: Graph Neural Networks; Machine Learning; Top quark physics, CMS Experiment

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