Karlsruhe 2024 – scientific programme
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T: Fachverband Teilchenphysik
T 103: Top physics 4 (tt+X)
T 103.3: Talk
Thursday, March 7, 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 Waßmer1 — 1Institute 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